Infusion systems and methods for patient activity adjustments

ABSTRACT

Infusion systems, infusion devices, and related operating methods are provided. An exemplary method of operating an infusion device capable of delivering fluid to a patient involves obtaining, by a control system associated with the infusion device, user input indicating an activity by the patient, obtaining historical data for the patient corresponding to the activity, determining a probable patient response corresponding to the activity based at least in part on the historical data for the patient, determining an adjustment for delivering the fluid by the infusion device based at least in part on the probable patient response, and operating the infusion device to deliver the fluid to the patient in accordance with the adjustment.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/437,536, filed Dec. 21, 2016, the entire contentof which is incorporated by reference herein.

TECHNICAL FIELD

Embodiments of the subject matter described herein relate generally tomedical devices, and more particularly, embodiments of the subjectmatter relate to automatically adapting operations of a fluid infusiondevice in a personalized manner.

BACKGROUND

Infusion pump devices and systems are relatively well known in themedical arts, for use in delivering or dispensing an agent, such asinsulin or another prescribed medication, to a patient. A typicalinfusion pump includes a pump drive system which typically includes asmall motor and drive train components that convert rotational motormotion to a translational displacement of a plunger (or stopper) in areservoir that delivers medication from the reservoir to the body of auser via a fluid path created between the reservoir and the body of auser. Use of infusion pump therapy has been increasing, especially fordelivering insulin for diabetics.

Continuous insulin infusion provides greater control of a diabetic'scondition, and hence, control schemes are being developed that allowinsulin infusion pumps to monitor and regulate a user's blood glucoselevel in a substantially continuous and autonomous manner, for example,overnight while the user is sleeping. Regulating blood glucose level iscomplicated by variations in the response time for the type of insulinbeing used along with each user's individual insulin response.Furthermore, a user's daily activities and experiences may cause thatuser's insulin response to vary throughout the course of a day or fromone day to the next. Thus, it is desirable to account for theanticipated variations or fluctuations in the user's insulin responsecaused by the user's activities or other condition(s) experienced by theuser.

Managing a diabetic's blood glucose level is also complicated by theuser's consumption of meals or carbohydrates. Often, a user manuallyadministers a bolus of insulin at or around meal time to mitigatepostprandial hyperglycemia. To effectively mitigate postprandialhyperglycemia while also avoiding postprandial hypoglycemia, the user isoften required to estimate the amount of carbohydrates being consumed,with that amount of carbohydrates then being utilized to determine theappropriate bolus dosage. While undesirably increasing the burden on thepatient for managing his or her therapy, manual errors such asmiscounting carbohydrates or failing to initiate a bolus in a timelymanner can also reduce the therapy effectiveness. Accordingly, there isa need facilitate improved glucose control that reduces the likelihoodof manual errors while also reducing patient workload.

BRIEF SUMMARY

An embodiment of a method of operating an infusion device capable ofdelivering fluid to a patient is provided. The method involvesobtaining, by a control system associated with the infusion device, userinput indicating an activity by the patient, obtaining, by the controlsystem, historical data for the patient corresponding to the activity,determining, by the control system, a probable patient responsecorresponding to the activity based at least in part on the historicaldata for the patient, determining, by the control system, an adjustmentfor delivering the fluid by the infusion device based at least in parton the probable patient response, and operating, by the control system,the infusion device to deliver the fluid to the patient in accordancewith the adjustment.

Another embodiment of a method of operating an infusion device capableof delivering insulin to a patient is provided. The method involvesobtaining, by a control system associated with the infusion device,input indicative of an activity by the patient in the future, obtaining,by the control system, historical glucose measurement data for thepatient corresponding to previous instances of the activity,determining, by the control system, a probable glycemic response to theactivity for the patient based at least in part on the historicalglucose measurement data for the previous instances of the activity,adjusting, by the control system, a closed-loop control parameter basedat least in part on the probable glycemic response, and after adjustingthe closed-loop control parameter, obtaining a sensor glucosemeasurement value for the patient and operating an actuation arrangementof the infusion device to deliver insulin to the patient based at leastin part on the sensor glucose measurement value and the adjustedclosed-loop control parameter.

In yet another embodiment, an infusion system is provided. The infusionsystem includes an actuation arrangement operable to deliver fluid to auser, the fluid influencing a physiological condition of the user, auser interface to receive input indicative of an activity by the user inthe future, a data storage element to maintain historical datacorresponding to previous instances of the activity by the user, and acontrol system coupled to the actuation arrangement, the data storageelement and the user interface to determine a probable physiologicalresponse by the user to the activity based at least in part on thehistorical data, determine a fluid delivery adjustment based on theprobable physiological response, and operate the actuation arrangementto deliver the fluid to the user in accordance with the fluid deliveryadjustment.

This summary is provided to introduce a selection of concepts in asimplified form that are further described below in the detaileddescription. This summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the subject matter may be derived byreferring to the detailed description and claims when considered inconjunction with the following figures, wherein like reference numbersrefer to similar elements throughout the figures, which may beillustrated for simplicity and clarity and are not necessarily drawn toscale.

FIG. 1 depicts an exemplary embodiment of an infusion system;

FIG. 2 depicts a plan view of an exemplary embodiment of a fluidinfusion device suitable for use in the infusion system of FIG. 1;

FIG. 3 is an exploded perspective view of the fluid infusion device ofFIG. 2;

FIG. 4 is a cross-sectional view of the fluid infusion device of FIGS.2-3 as viewed along line 4-4 in FIG. 3 when assembled with a reservoirinserted in the infusion device;

FIG. 5 is a block diagram of an exemplary infusion system suitable foruse with a fluid infusion device in one or more embodiments;

FIG. 6 is a block diagram of an exemplary pump control system suitablefor use in the infusion device in the infusion system of FIG. 5 in oneor more embodiments;

FIG. 7 is a block diagram of a closed-loop control system that may beimplemented or otherwise supported by the pump control system in thefluid infusion device of FIGS. 5-6 in one or more exemplary embodiments;

FIG. 8 is a block diagram of an exemplary patient monitoring system;

FIG. 9 is a flow diagram of an exemplary prospective closed-loop controlprocess suitable implementation in connection with an infusion device inone or more exemplary embodiments;

FIG. 10 is a flow diagram of an exemplary event pattern control suitableimplementation in connection with an infusion device in one or moreexemplary embodiments;

FIG. 11 is a flow diagram of an exemplary personalized bolus processsuitable implementation in connection with an infusion device in one ormore exemplary embodiments;

FIG. 12 is a flow diagram of an exemplary nutritional bolus processsuitable implementation in connection with an infusion device in one ormore exemplary embodiments;

FIG. 13 is a flow diagram of an exemplary patient activity controlprocess suitable implementation in connection with an infusion device inone or more exemplary embodiments;

FIGS. 14-15 depict exemplary graphical user interface displays that maybe presented on an electronic device in connection with one or more ofthe processes of FIGS. 9-13;

FIG. 16 depicts an exemplary graphical user interface display that maybe presented on an infusion device in connection with the event patterncontrol process of FIG. 10;

FIG. 17 depicts an exemplary graphical user interface display that maybe presented on a client computing device in connection with the eventpattern control process of FIG. 10; and

FIG. 18 depicts an embodiment of a computing device of a diabetes datamanagement system suitable for use in connection with any one or more ofthe systems of FIGS. 1, 5 and 8 and any one or more of the processes ofFIGS. 9-13 in accordance with one or more embodiments.

DETAILED DESCRIPTION

The following detailed description is merely illustrative in nature andis not intended to limit the embodiments of the subject matter or theapplication and uses of such embodiments. As used herein, the word“exemplary” means “serving as an example, instance, or illustration.”Any implementation described herein as exemplary is not necessarily tobe construed as preferred or advantageous over other implementations.Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description.

While the subject matter described herein can be implemented in anyelectronic device that includes a motor, exemplary embodiments describedbelow are implemented in the form of medical devices, such as portableelectronic medical devices. Although many different applications arepossible, the following description focuses on a fluid infusion device(or infusion pump) as part of an infusion system deployment. For thesake of brevity, conventional techniques related to infusion systemoperation, insulin pump and/or infusion set operation, and otherfunctional aspects of the systems (and the individual operatingcomponents of the systems) may not be described in detail here. Examplesof infusion pumps may be of the type described in, but not limited to,U.S. Pat. Nos. 4,562,751; 4,685,903; 5,080,653; 5,505,709; 5,097,122;6,485,465; 6,554,798; 6,558,320; 6,558,351; 6,641,533; 6,659,980;6,752,787; 6,817,990; 6,932,584; and 7,621,893; each of which are hereinincorporated by reference.

Embodiments of the subject matter described herein generally relate tofluid infusion devices including a motor or other actuation arrangementthat is operable to linearly displace a plunger (or stopper) of areservoir provided within the fluid infusion device to deliver a dosageof fluid, such as insulin, to the body of a user. In one or moreexemplary embodiments, delivery commands (or dosage commands) thatgovern operation of the motor are determined based on a differencebetween a measured value for a physiological condition in the body ofthe user and a target value using closed-loop control to regulate themeasured value to the target value. As described in greater detailbelow, in one or more embodiments, a meal, exercise, or other activityor event that is likely to influence the user's response (orsensitivity) to the fluid being administered is detected or otherwiseidentified, and at least some of the control information utilized by theclosed-loop control to generate delivery commands and operate theinfusion device is automatically adjusted to account for the anticipatedchange in the user's response to the fluid.

For purposes of explanation, the subject matter may be described hereinprimarily in the context of identifying or detecting a meal for purposesof regulating a glucose level in the body of the user by administeringdosages of insulin that account for the meal in a personalized manner.That said, the subject matter described herein is not necessarilylimited to glucose regulation, insulin infusion, or meals, and inpractice, could be implemented in an equivalent manner with respect toother medications, physiological conditions, exercise or otheractivities, and/or the like.

As described in greater detail below in the context of FIG. 9, in one ormore embodiments, closed-loop control information is adjusted in advanceof an anticipated meal, activity, or other event likely to influence theuser's glucose levels or insulin response. In this regard, prospectiveclosed-loop control parameter adjustments account for the relativelyslow action of long-acting subcutaneously administered insulin byadjusting insulin delivery in advance of a meal in a manner thatmitigates postprandial hyperglycemia. Based on historical meal dataassociated with the user, the likelihood of a future meal event within aspecific time period in advance of the current time can beprobabilistically determined. The probability of a future meal eventcould be based on all past meal events for the user or based on a subsetof historical meal events corresponding to the current context (e.g.,historical meals from weekdays only, historical meals on weekends only,historical meals on the specific day of the week, time of the day, mealcontent, and/or the like). If the probability of a future meal eventwithin the forecast time period in advance of the current time (e.g.,within the next two hours) is greater than a threshold percentage, oneor more closed-loop control parameters are automatically adjusted in amanner that is likely to reduce the user's glucose level (or increaseyet to be metabolized insulin on board) prior to start of mealconsumption. For example, the reference or target glucose value utilizedby the closed-loop control algorithm may be reduced to increase insulindelivery. By prospectively adjusting the closed-loop controls inanticipation of a meal, the user may not necessarily be required toproactively and manually deliver a meal bolus or perform otherpreparatory actions in advance of the meal.

As described in greater detail below in the context of FIG. 10, in oneor more embodiments, historical data associated with the user (e.g.,historical measurement data, meal data, exercise data, and the like) isanalyzed to identify behavior patterns that exhibit a correspondingphysiological response. The user's likely engagement in a particularevent or activity that is likely to influence the user's glucose levelor insulin response is automatically detected based on the correlationbetween the user's current or recent measurement data and event patternexhibited by the historical measurement data associated with thatparticular event or activity. In response to detecting an event patternin the user's current or recent measurement data, a user notificationindicative of the detected event may be automatically generated. In thisregard, the user notification may indicate the type of event detected,and potentially other characteristics associated with the event. Forexample, in the case of a meal event, the user notification may indicatethe detected event is a meal and include a detected meal size and/ormeal type associated with the detected event pattern. In response toreceiving confirmation of the detected event, one or more aspects ofoperation of the infusion device are automatically adjusted to deliverinsulin to the user in a manner that is influenced by the event type andother characteristics associated with the event.

For example, historical data associated with the user may be utilized tocorrelate sensor glucose measurement patterns for that particular userto a particular size and/or type of meal consumed by the user. Inresponse to detecting a subset of the user's recent measurement datacorresponds to the historical measurement data pattern associated with aparticular size and type of meal, a user notification may beautomatically generated that prompts the user to confirm that he or shehas or is consuming that particular size and type of meal. In someembodiments, in response to receiving user confirmation, a meal bolusmay be automatically determined based on the confirmed meal size andmeal type without requiring any carbohydrate counting or other action bythe user.

As described in greater detail below in the context of FIGS. 11-12, inone or more exemplary embodiments, meal bolus dosages are calculated orotherwise determined in a personalized, patient-specific manner. Ratherthan counting carbohydrates or otherwise quantifying a meal size, theuser can input a qualitative meal size, such as small, medium, large,and the like. Based on the user's historical meal data and historicalglucose measurement data associated with a particular meal size, apatient-specific carbohydrate ratio associated with that meal size canbe determined that accounts for variability in the user's physiologicalresponse to meals having that associated size. For example, the rate ofglucose appearance after a meal can be influenced by multiple factors,such as the time of day of meal consumption, the particular day of theweek, and the order of consumption of meal components for nonhomogeneousmeals (e.g., meat before vegetables or vice versa).

In one or more embodiments, historical meal data and contemporaneoushistorical measurement data are analyzed to mathematically model andoptimize the patient-specific carbohydrate ratio for a particular mealsize based on that particular patient's postprandial pharmacodynamicsfollowing meals of that particular size. In this regard, theoptimization may be configured to account for observed diversity in themeal related glucose rate of appearance while also modeling safety andeffectiveness of the carbohydrate ratio when utilized for closed-loopcontrol. Based on the patient's historical meal data, an estimatedpatient-specific carbohydrate amount corresponding to an input meal sizemay also be determined based on the user's historical meal behavior.Then, in response to an input meal size, the patient-specific estimatedcarbohydrate amount for that input meal size and the patient-specificcarbohydrate ratio associated with that input meal size may be utilizedto determine a meal bolus dosage amount in a personalized manner withoutany carbohydrate counting.

In one or more embodiments, the personalized meal bolus dosage amount orcontrol information associated with the closed-loop operating mode maybe further adjusted or modified to account for the particularnutritional content of the meal (or meal type) being consumed. Tofacilitate meal type adjustments and reduce the user burden, apersonalized meal library is created for an individual user based on hisor her meal history, and recommended or suggested meals can beprioritized based on correlations to current contextual information(e.g., time of day, day of week, geographic location, etc.). Oncesufficient historical meal data for a user exists, a personalizedlibrary of likely meal content can be created, with machine learningbeing utilized to predict the most likely meal content and serving sizesfor a meal at the current time (or an anticipated meal in the future)based on the user's historical meal data and the current contextualsituation (e.g., the current time of day, current day of week, currentgeographic location, etc.). A user notification may be provided thatincludes or otherwise indicates the predicted meal content and size tothe user (or an ordered listing of the most likely combinations of mealcontent and sizes), thereby allowing the user to quickly andconveniently confirm the meal content and size, and without anycarbohydrate counting or browsing a list or library of meals when theprediction is correct. Based on the validation or modification of thepredicted meal content, the user's historical meal data or predictionmodel can be dynamically updated in a manner that allows for theaccuracy of the predicted meal content to improve over time.

Once the meal content is identified, the bolus dosage amount, bolusdosage schedule, or closed-loop control information may be modified oradjusted to account for the nutritional characteristics of the meal. Forexample, for a meal earlier in the day including relatively fast actingcarbohydrates (e.g., a high carbohydrate breakfast), the bolus dosageamount may be increased (e.g., by scaling the carbohydrate amount by avalue greater than one) while also automatically modifying theclosed-loop control settings to suspend insulin delivery for at least aminimum suspension threshold amount of time. Conversely, for arelatively high fat meal late in the day (e.g., a high fat dinner), thebolus dosage amount may be decreased while also modifying theclosed-loop control settings to increase insulin delivery for apostprandial time period (e.g., by temporarily decreasing the glucosetarget for the closed-loop control).

As described in greater detail below in the context of FIG. 13, in oneor more exemplary embodiments, the bolus dosage amount, bolus dosageschedule, or closed-loop control information may also be modified oradjusted to account for contemporaneous or future activity by thepatient. For example, upon the user confirming a meal, administering ameal bolus, or the like, the user may be prompted to provide inputpertaining to the user's current or likely activity in the future. Inthis regard, the user may provide input confirming or indicating whetherhe or she is or will likely be engaging in exercise, sleep, work, orother activities that may influence the user's glycemic response. Basedon the input activity, the bolus dosage or closed-loop controls may beadjusted to account for the user's predicted physiological response tothe input activity based on the user's historical physiological responseto that particular type of activity using the user's historical sensorglucose measurement data. Additional contextual information (e.g., timeof day, day of week, geographic location, and the like) may also beincorporated to further refine the user's predicted physiologicalresponse. Bolus dosages or closed-loop control parameters may then beadjusted to account for the activities that the user is or is likely tobe engaged in.

Infusion System Overview

Turning now to FIG. 1, one exemplary embodiment of an infusion system100 includes, without limitation, a fluid infusion device (or infusionpump) 102, a sensing arrangement 104, a command control device (CCD)106, and a computer 108. The components of an infusion system 100 may berealized using different platforms, designs, and configurations, and theembodiment shown in FIG. 1 is not exhaustive or limiting. In practice,the infusion device 102 and the sensing arrangement 104 are secured atdesired locations on the body of a user (or patient), as illustrated inFIG. 1. In this regard, the locations at which the infusion device 102and the sensing arrangement 104 are secured to the body of the user inFIG. 1 are provided only as a representative, non-limiting, example. Theelements of the infusion system 100 may be similar to those described inU.S. Pat. No. 8,674,288, the subject matter of which is herebyincorporated by reference in its entirety.

In the illustrated embodiment of FIG. 1, the infusion device 102 isdesigned as a portable medical device suitable for infusing a fluid, aliquid, a gel, or other medicament into the body of a user. In exemplaryembodiments, the infused fluid is insulin, although many other fluidsmay be administered through infusion such as, but not limited to, HIVdrugs, drugs to treat pulmonary hypertension, iron chelation drugs, painmedications, anti-cancer treatments, medications, vitamins, hormones, orthe like. In some embodiments, the fluid may include a nutritionalsupplement, a dye, a tracing medium, a saline medium, a hydrationmedium, or the like.

The sensing arrangement 104 generally represents the components of theinfusion system 100 configured to sense, detect, measure or otherwisequantify a condition of the user, and may include a sensor, a monitor,or the like, for providing data indicative of the condition that issensed, detected, measured or otherwise monitored by the sensingarrangement. In this regard, the sensing arrangement 104 may includeelectronics and enzymes reactive to a biological condition, such as ablood glucose level, or the like, of the user, and provide dataindicative of the blood glucose level to the infusion device 102, theCCD 106 and/or the computer 108. For example, the infusion device 102,the CCD 106 and/or the computer 108 may include a display for presentinginformation or data to the user based on the sensor data received fromthe sensing arrangement 104, such as, for example, a current glucoselevel of the user, a graph or chart of the user's glucose level versustime, device status indicators, alert messages, or the like. In otherembodiments, the infusion device 102, the CCD 106 and/or the computer108 may include electronics and software that are configured to analyzesensor data and operate the infusion device 102 to deliver fluid to thebody of the user based on the sensor data and/or preprogrammed deliveryroutines. Thus, in exemplary embodiments, one or more of the infusiondevice 102, the sensing arrangement 104, the CCD 106, and/or thecomputer 108 includes a transmitter, a receiver, and/or othertransceiver electronics that allow for communication with othercomponents of the infusion system 100, so that the sensing arrangement104 may transmit sensor data or monitor data to one or more of theinfusion device 102, the CCD 106 and/or the computer 108.

Still referring to FIG. 1, in various embodiments, the sensingarrangement 104 may be secured to the body of the user or embedded inthe body of the user at a location that is remote from the location atwhich the infusion device 102 is secured to the body of the user. Invarious other embodiments, the sensing arrangement 104 may beincorporated within the infusion device 102. In other embodiments, thesensing arrangement 104 may be separate and apart from the infusiondevice 102, and may be, for example, part of the CCD 106. In suchembodiments, the sensing arrangement 104 may be configured to receive abiological sample, analyte, or the like, to measure a condition of theuser.

In some embodiments, the CCD 106 and/or the computer 108 may includeelectronics and other components configured to perform processing,delivery routine storage, and to control the infusion device 102 in amanner that is influenced by sensor data measured by and/or receivedfrom the sensing arrangement 104. By including control functions in theCCD 106 and/or the computer 108, the infusion device 102 may be madewith more simplified electronics. However, in other embodiments, theinfusion device 102 may include all control functions, and may operatewithout the CCD 106 and/or the computer 108. In various embodiments, theCCD 106 may be a portable electronic device. In addition, in variousembodiments, the infusion device 102 and/or the sensing arrangement 104may be configured to transmit data to the CCD 106 and/or the computer108 for display or processing of the data by the CCD 106 and/or thecomputer 108.

In some embodiments, the CCD 106 and/or the computer 108 may provideinformation to the user that facilitates the user's subsequent use ofthe infusion device 102. For example, the CCD 106 may provideinformation to the user to allow the user to determine the rate or doseof medication to be administered into the user's body. In otherembodiments, the CCD 106 may provide information to the infusion device102 to autonomously control the rate or dose of medication administeredinto the body of the user. In some embodiments, the sensing arrangement104 may be integrated into the CCD 106. Such embodiments may allow theuser to monitor a condition by providing, for example, a sample of hisor her blood to the sensing arrangement 104 to assess his or hercondition. In some embodiments, the sensing arrangement 104 and the CCD106 may be used for determining glucose levels in the blood and/or bodyfluids of the user without the use of, or necessity of, a wire or cableconnection between the infusion device 102 and the sensing arrangement104 and/or the CCD 106.

In some embodiments, the sensing arrangement 104 and/or the infusiondevice 102 are cooperatively configured to utilize a closed-loop systemfor delivering fluid to the user. Examples of sensing devices and/orinfusion pumps utilizing closed-loop systems may be found at, but arenot limited to, the following U.S. Pat. Nos. 6,088,608, 6,119,028,6,589,229, 6,740,072, 6,827,702, 7,323,142, and 7,402,153 or UnitedStates Patent Application Publication No. 2014/0066889, all of which areincorporated herein by reference in their entirety. In such embodiments,the sensing arrangement 104 is configured to sense or measure acondition of the user, such as, blood glucose level or the like. Theinfusion device 102 is configured to deliver fluid in response to thecondition sensed by the sensing arrangement 104. In turn, the sensingarrangement 104 continues to sense or otherwise quantify a currentcondition of the user, thereby allowing the infusion device 102 todeliver fluid continuously in response to the condition currently (ormost recently) sensed by the sensing arrangement 104 indefinitely. Insome embodiments, the sensing arrangement 104 and/or the infusion device102 may be configured to utilize the closed-loop system only for aportion of the day, for example only when the user is asleep or awake.

FIGS. 2-4 depict one exemplary embodiment of a fluid infusion device 200(or alternatively, infusion pump) suitable for use in an infusionsystem, such as, for example, as infusion device 102 in the infusionsystem 100 of FIG. 1. The fluid infusion device 200 is a portablemedical device designed to be carried or worn by a patient (or user),and the fluid infusion device 200 may leverage any number ofconventional features, components, elements, and characteristics ofexisting fluid infusion devices, such as, for example, some of thefeatures, components, elements, and/or characteristics described in U.S.Pat. Nos. 6,485,465 and 7,621,893. It should be appreciated that FIGS.2-4 depict some aspects of the infusion device 200 in a simplifiedmanner; in practice, the infusion device 200 could include additionalelements, features, or components that are not shown or described indetail herein.

As best illustrated in FIGS. 2-3, the illustrated embodiment of thefluid infusion device 200 includes a housing 202 adapted to receive afluid-containing reservoir 205. An opening 220 in the housing 202accommodates a fitting 223 (or cap) for the reservoir 205, with thefitting 223 being configured to mate or otherwise interface with tubing221 of an infusion set 225 that provides a fluid path to/from the bodyof the user. In this manner, fluid communication from the interior ofthe reservoir 205 to the user is established via the tubing 221. Theillustrated fluid infusion device 200 includes a human-machine interface(HMI) 230 (or user interface) that includes elements 232, 234 that canbe manipulated by the user to administer a bolus of fluid (e.g.,insulin), to change therapy settings, to change user preferences, toselect display features, and the like. The infusion device also includesa display element 226, such as a liquid crystal display (LCD) or anothersuitable display element, that can be used to present various types ofinformation or data to the user, such as, without limitation: thecurrent glucose level of the patient; the time; a graph or chart of thepatient's glucose level versus time; device status indicators; etc.

The housing 202 is formed from a substantially rigid material having ahollow interior 214 adapted to allow an electronics assembly 204, asliding member (or slide) 206, a drive system 208, a sensor assembly210, and a drive system capping member 212 to be disposed therein inaddition to the reservoir 205, with the contents of the housing 202being enclosed by a housing capping member 216. The opening 220, theslide 206, and the drive system 208 are coaxially aligned in an axialdirection (indicated by arrow 218), whereby the drive system 208facilitates linear displacement of the slide 206 in the axial direction218 to dispense fluid from the reservoir 205 (after the reservoir 205has been inserted into opening 220), with the sensor assembly 210 beingconfigured to measure axial forces (e.g., forces aligned with the axialdirection 218) exerted on the sensor assembly 210 responsive tooperating the drive system 208 to displace the slide 206. In variousembodiments, the sensor assembly 210 may be utilized to detect one ormore of the following: an occlusion in a fluid path that slows,prevents, or otherwise degrades fluid delivery from the reservoir 205 toa user's body; when the reservoir 205 is empty; when the slide 206 isproperly seated with the reservoir 205; when a fluid dose has beendelivered; when the infusion pump 200 is subjected to shock orvibration; when the infusion pump 200 requires maintenance.

Depending on the embodiment, the fluid-containing reservoir 205 may berealized as a syringe, a vial, a cartridge, a bag, or the like. Incertain embodiments, the infused fluid is insulin, although many otherfluids may be administered through infusion such as, but not limited to,HIV drugs, drugs to treat pulmonary hypertension, iron chelation drugs,pain medications, anti-cancer treatments, medications, vitamins,hormones, or the like. As best illustrated in FIGS. 3-4, the reservoir205 typically includes a reservoir barrel 219 that contains the fluidand is concentrically and/or coaxially aligned with the slide 206 (e.g.,in the axial direction 218) when the reservoir 205 is inserted into theinfusion pump 200. The end of the reservoir 205 proximate the opening220 may include or otherwise mate with the fitting 223, which securesthe reservoir 205 in the housing 202 and prevents displacement of thereservoir 205 in the axial direction 218 with respect to the housing 202after the reservoir 205 is inserted into the housing 202. As describedabove, the fitting 223 extends from (or through) the opening 220 of thehousing 202 and mates with tubing 221 to establish fluid communicationfrom the interior of the reservoir 205 (e.g., reservoir barrel 219) tothe user via the tubing 221 and infusion set 225. The opposing end ofthe reservoir 205 proximate the slide 206 includes a plunger 217 (orstopper) positioned to push fluid from inside the barrel 219 of thereservoir 205 along a fluid path through tubing 221 to a user. The slide206 is configured to mechanically couple or otherwise engage with theplunger 217, thereby becoming seated with the plunger 217 and/orreservoir 205. Fluid is forced from the reservoir 205 via tubing 221 asthe drive system 208 is operated to displace the slide 206 in the axialdirection 218 toward the opening 220 in the housing 202.

In the illustrated embodiment of FIGS. 3-4, the drive system 208includes a motor assembly 207 and a drive screw 209. The motor assembly207 includes a motor that is coupled to drive train components of thedrive system 208 that are configured to convert rotational motor motionto a translational displacement of the slide 206 in the axial direction218, and thereby engaging and displacing the plunger 217 of thereservoir 205 in the axial direction 218. In some embodiments, the motorassembly 207 may also be powered to translate the slide 206 in theopposing direction (e.g., the direction opposite direction 218) toretract and/or detach from the reservoir 205 to allow the reservoir 205to be replaced. In exemplary embodiments, the motor assembly 207includes a brushless DC (BLDC) motor having one or more permanentmagnets mounted, affixed, or otherwise disposed on its rotor. However,the subject matter described herein is not necessarily limited to usewith BLDC motors, and in alternative embodiments, the motor may berealized as a solenoid motor, an AC motor, a stepper motor, apiezoelectric caterpillar drive, a shape memory actuator drive, anelectrochemical gas cell, a thermally driven gas cell, a bimetallicactuator, or the like. The drive train components may comprise one ormore lead screws, cams, ratchets, jacks, pulleys, pawls, clamps, gears,nuts, slides, bearings, levers, beams, stoppers, plungers, sliders,brackets, guides, bearings, supports, bellows, caps, diaphragms, bags,heaters, or the like. In this regard, although the illustratedembodiment of the infusion pump utilizes a coaxially aligned drivetrain, the motor could be arranged in an offset or otherwise non-coaxialmanner, relative to the longitudinal axis of the reservoir 205.

As best shown in FIG. 4, the drive screw 209 mates with threads 402internal to the slide 206. When the motor assembly 207 is powered andoperated, the drive screw 209 rotates, and the slide 206 is forced totranslate in the axial direction 218. In an exemplary embodiment, theinfusion pump 200 includes a sleeve 211 to prevent the slide 206 fromrotating when the drive screw 209 of the drive system 208 rotates. Thus,rotation of the drive screw 209 causes the slide 206 to extend orretract relative to the drive motor assembly 207. When the fluidinfusion device is assembled and operational, the slide 206 contacts theplunger 217 to engage the reservoir 205 and control delivery of fluidfrom the infusion pump 200. In an exemplary embodiment, the shoulderportion 215 of the slide 206 contacts or otherwise engages the plunger217 to displace the plunger 217 in the axial direction 218. Inalternative embodiments, the slide 206 may include a threaded tip 213capable of being detachably engaged with internal threads 404 on theplunger 217 of the reservoir 205, as described in detail in U.S. Pat.Nos. 6,248,093 and 6,485,465, which are incorporated by referenceherein.

As illustrated in FIG. 3, the electronics assembly 204 includes controlelectronics 224 coupled to the display element 226, with the housing 202including a transparent window portion 228 that is aligned with thedisplay element 226 to allow the display 226 to be viewed by the userwhen the electronics assembly 204 is disposed within the interior 214 ofthe housing 202. The control electronics 224 generally represent thehardware, firmware, processing logic and/or software (or combinationsthereof) configured to control operation of the motor assembly 207and/or drive system 208, as described in greater detail below in thecontext of FIG. 5. Whether such functionality is implemented ashardware, firmware, a state machine, or software depends upon theparticular application and design constraints imposed on the embodiment.Those familiar with the concepts described here may implement suchfunctionality in a suitable manner for each particular application, butsuch implementation decisions should not be interpreted as beingrestrictive or limiting. In an exemplary embodiment, the controlelectronics 224 includes one or more programmable controllers that maybe programmed to control operation of the infusion pump 200.

The motor assembly 207 includes one or more electrical leads 236 adaptedto be electrically coupled to the electronics assembly 204 to establishcommunication between the control electronics 224 and the motor assembly207. In response to command signals from the control electronics 224that operate a motor driver (e.g., a power converter) to regulate theamount of power supplied to the motor from a power supply, the motoractuates the drive train components of the drive system 208 to displacethe slide 206 in the axial direction 218 to force fluid from thereservoir 205 along a fluid path (including tubing 221 and an infusionset), thereby administering doses of the fluid contained in thereservoir 205 into the user's body. Preferably, the power supply isrealized one or more batteries contained within the housing 202.Alternatively, the power supply may be a solar panel, capacitor, AC orDC power supplied through a power cord, or the like. In someembodiments, the control electronics 224 may operate the motor of themotor assembly 207 and/or drive system 208 in a stepwise manner,typically on an intermittent basis; to administer discrete precise dosesof the fluid to the user according to programmed delivery profiles.

Referring to FIGS. 2-4, as described above, the user interface 230includes HMI elements, such as buttons 232 and a directional pad 234,that are formed on a graphic keypad overlay 231 that overlies a keypadassembly 233, which includes features corresponding to the buttons 232,directional pad 234 or other user interface items indicated by thegraphic keypad overlay 231. When assembled, the keypad assembly 233 iscoupled to the control electronics 224, thereby allowing the HMIelements 232, 234 to be manipulated by the user to interact with thecontrol electronics 224 and control operation of the infusion pump 200,for example, to administer a bolus of insulin, to change therapysettings, to change user preferences, to select display features, to setor disable alarms and reminders, and the like. In this regard, thecontrol electronics 224 maintains and/or provides information to thedisplay 226 regarding program parameters, delivery profiles, pumpoperation, alarms, warnings, statuses, or the like, which may beadjusted using the HMI elements 232, 234. In various embodiments, theHMI elements 232, 234 may be realized as physical objects (e.g.,buttons, knobs, joysticks, and the like) or virtual objects (e.g., usingtouch-sensing and/or proximity-sensing technologies). For example, insome embodiments, the display 226 may be realized as a touch screen ortouch-sensitive display, and in such embodiments, the features and/orfunctionality of the HMI elements 232, 234 may be integrated into thedisplay 226 and the HMI 230 may not be present. In some embodiments, theelectronics assembly 204 may also include alert generating elementscoupled to the control electronics 224 and suitably configured togenerate one or more types of feedback, such as, without limitation:audible feedback; visual feedback; haptic (physical) feedback; or thelike.

Referring to FIGS. 3-4, in accordance with one or more embodiments, thesensor assembly 210 includes a back plate structure 250 and a loadingelement 260. The loading element 260 is disposed between the cappingmember 212 and a beam structure 270 that includes one or more beamshaving sensing elements disposed thereon that are influenced bycompressive force applied to the sensor assembly 210 that deflects theone or more beams, as described in greater detail in U.S. Pat. No.8,474,332, which is incorporated by reference herein. In exemplaryembodiments, the back plate structure 250 is affixed, adhered, mounted,or otherwise mechanically coupled to the bottom surface 238 of the drivesystem 208 such that the back plate structure 250 resides between thebottom surface 238 of the drive system 208 and the housing cap 216. Thedrive system capping member 212 is contoured to accommodate and conformto the bottom of the sensor assembly 210 and the drive system 208. Thedrive system capping member 212 may be affixed to the interior of thehousing 202 to prevent displacement of the sensor assembly 210 in thedirection opposite the direction of force provided by the drive system208 (e.g., the direction opposite direction 218). Thus, the sensorassembly 210 is positioned between the motor assembly 207 and secured bythe capping member 212, which prevents displacement of the sensorassembly 210 in a downward direction opposite the direction of arrow218, such that the sensor assembly 210 is subjected to a reactionarycompressive force when the drive system 208 and/or motor assembly 207 isoperated to displace the slide 206 in the axial direction 218 inopposition to the fluid pressure in the reservoir 205. Under normaloperating conditions, the compressive force applied to the sensorassembly 210 is correlated with the fluid pressure in the reservoir 205.As shown, electrical leads 240 are adapted to electrically couple thesensing elements of the sensor assembly 210 to the electronics assembly204 to establish communication to the control electronics 224, whereinthe control electronics 224 are configured to measure, receive, orotherwise obtain electrical signals from the sensing elements of thesensor assembly 210 that are indicative of the force applied by thedrive system 208 in the axial direction 218.

FIG. 5 depicts an exemplary embodiment of an infusion system 500suitable for use with an infusion device 502, such as any one of theinfusion devices 102, 200 described above. The infusion system 500 iscapable of controlling or otherwise regulating a physiological conditionin the body 501 of a user to a desired (or target) value or otherwisemaintain the condition within a range of acceptable values in anautomated or autonomous manner. In one or more exemplary embodiments,the condition being regulated is sensed, detected, measured or otherwisequantified by a sensing arrangement 504 (e.g., sensing arrangement 504)communicatively coupled to the infusion device 502. However, it shouldbe noted that in alternative embodiments, the condition being regulatedby the infusion system 500 may be correlative to the measured valuesobtained by the sensing arrangement 504. That said, for clarity andpurposes of explanation, the subject matter may be described herein inthe context of the sensing arrangement 504 being realized as a glucosesensing arrangement that senses, detects, measures or otherwisequantifies the user's glucose level, which is being regulated in thebody 501 of the user by the infusion system 500.

In exemplary embodiments, the sensing arrangement 504 includes one ormore interstitial glucose sensing elements that generate or otherwiseoutput electrical signals (alternatively referred to herein asmeasurement signals) having a signal characteristic that is correlativeto, influenced by, or otherwise indicative of the relative interstitialfluid glucose level in the body 501 of the user. The output electricalsignals are filtered or otherwise processed to obtain a measurementvalue indicative of the user's interstitial fluid glucose level. Inexemplary embodiments, a blood glucose meter 530, such as a finger stickdevice, is utilized to directly sense, detect, measure or otherwisequantify the blood glucose in the body 501 of the user. In this regard,the blood glucose meter 530 outputs or otherwise provides a measuredblood glucose value that may be utilized as a reference measurement forcalibrating the sensing arrangement 504 and converting a measurementvalue indicative of the user's interstitial fluid glucose level into acorresponding calibrated blood glucose value. For purposes ofexplanation, the calibrated blood glucose value calculated based on theelectrical signals output by the sensing element(s) of the sensingarrangement 504 may alternatively be referred to herein as the sensorglucose value, the sensed glucose value, or variants thereof.

In exemplary embodiments, the infusion system 500 also includes one ormore additional sensing arrangements 506, 508 configured to sense,detect, measure or otherwise quantify a characteristic of the body 501of the user that is indicative of a condition in the body 501 of theuser. In this regard, in addition to the glucose sensing arrangement504, one or more auxiliary sensing arrangements 506 may be worn,carried, or otherwise associated with the body 501 of the user tomeasure characteristics or conditions of the user (or the user'sactivity) that may influence the user's glucose levels or insulinsensitivity. For example, a heart rate sensing arrangement 506 could beworn on or otherwise associated with the user's body 501 to sense,detect, measure or otherwise quantify the user's heart rate, which, inturn, may be indicative of exercise (and the intensity thereof) that islikely to influence the user's glucose levels or insulin response in thebody 501. In yet another embodiment, another invasive, interstitial, orsubcutaneous sensing arrangement 506 may be inserted into the body 501of the user to obtain measurements of another physiological conditionthat may be indicative of exercise (and the intensity thereof), such as,for example, a lactate sensor, a ketone sensor, or the like. Dependingon the embodiment, the auxiliary sensing arrangement(s) 506 could berealized as a standalone component worn by the user, or alternatively,the auxiliary sensing arrangement(s) 506 may be integrated with theinfusion device 502 or the glucose sensing arrangement 504.

The illustrated infusion system 500 also includes an accelerationsensing arrangement 508 (or accelerometer) that may be worn on orotherwise associated with the user's body 501 to sense, detect, measureor otherwise quantify an acceleration of the user's body 501, which, inturn, may be indicative of exercise or some other condition in the body501 that is likely to influence the user's insulin response. While theacceleration sensing arrangement 508 is depicted as being integratedinto the infusion device 502 in FIG. 5, in alternative embodiments, theacceleration sensing arrangement 508 may be integrated with anothersensing arrangement 504, 506 on the body 501 of the user, or theacceleration sensing arrangement 508 may be realized as a separatestandalone component that is worn by the user.

In the illustrated embodiment, the pump control system 520 generallyrepresents the electronics and other components of the infusion device502 that control operation of the fluid infusion device 502 according toa desired infusion delivery program in a manner that is influenced bythe sensed glucose value indicating the current glucose level in thebody 501 of the user. For example, to support a closed-loop operatingmode, the pump control system 520 maintains, receives, or otherwiseobtains a target or commanded glucose value, and automatically generatesor otherwise determines dosage commands for operating an actuationarrangement, such as a motor 532, to displace the plunger 517 anddeliver insulin to the body 501 of the user based on the differencebetween the sensed glucose value and the target glucose value. In otheroperating modes, the pump control system 520 may generate or otherwisedetermine dosage commands configured to maintain the sensed glucosevalue below an upper glucose limit, above a lower glucose limit, orotherwise within a desired range of glucose values. In practice, theinfusion device 502 may store or otherwise maintain the target value,upper and/or lower glucose limit(s), insulin delivery limit(s), and/orother glucose threshold value(s) in a data storage element accessible tothe pump control system 520. As described in greater detail, in one ormore exemplary embodiments, the pump control system 520 automaticallyadjusts or adapts one or more parameters or other control informationused to generate commands for operating the motor 532 in a manner thataccounts for a likely change in the user's glucose level or insulinresponse resulting from a meal, exercise, or other activity.

Still referring to FIG. 5, the target glucose value and other thresholdglucose values utilized by the pump control system 520 may be receivedfrom an external component (e.g., CCD 106 and/or computing device 108)or be input by a user via a user interface element 540 associated withthe infusion device 502. In practice, the one or more user interfaceelement(s) 540 associated with the infusion device 502 typically includeat least one input user interface element, such as, for example, abutton, a keypad, a keyboard, a knob, a joystick, a mouse, a touchpanel, a touchscreen, a microphone or another audio input device, and/orthe like. Additionally, the one or more user interface element(s) 540include at least one output user interface element, such as, forexample, a display element (e.g., a light-emitting diode or the like), adisplay device (e.g., a liquid crystal display or the like), a speakeror another audio output device, a haptic feedback device, or the like,for providing notifications or other information to the user. It shouldbe noted that although FIG. 5 depicts the user interface element(s) 540as being separate from the infusion device 502, in practice, one or moreof the user interface element(s) 540 may be integrated with the infusiondevice 502. Furthermore, in some embodiments, one or more user interfaceelement(s) 540 are integrated with the sensing arrangement 504 inaddition to and/or in alternative to the user interface element(s) 540integrated with the infusion device 502. The user interface element(s)540 may be manipulated by the user to operate the infusion device 502 todeliver correction boluses, adjust target and/or threshold values,modify the delivery control scheme or operating mode, and the like, asdesired.

Still referring to FIG. 5, in the illustrated embodiment, the infusiondevice 502 includes a motor control module 512 coupled to a motor 532(e.g., motor assembly 207) that is operable to displace a plunger 517(e.g., plunger 217) in a reservoir (e.g., reservoir 205) and provide adesired amount of fluid to the body 501 of a user. In this regard,displacement of the plunger 517 results in the delivery of a fluid, suchas insulin, that is capable of influencing the user's physiologicalcondition to the body 501 of the user via a fluid delivery path (e.g.,via tubing 221 of an infusion set 225). A motor driver module 514 iscoupled between an energy source 518 and the motor 532. The motorcontrol module 512 is coupled to the motor driver module 514, and themotor control module 512 generates or otherwise provides command signalsthat operate the motor driver module 514 to provide current (or power)from the energy source 518 to the motor 532 to displace the plunger 517in response to receiving, from a pump control system 520, a dosagecommand indicative of the desired amount of fluid to be delivered.

In exemplary embodiments, the energy source 518 is realized as a batteryhoused within the infusion device 502 (e.g., within housing 202) thatprovides direct current (DC) power. In this regard, the motor drivermodule 514 generally represents the combination of circuitry, hardwareand/or other electrical components configured to convert or otherwisetransfer DC power provided by the energy source 518 into alternatingelectrical signals applied to respective phases of the stator windingsof the motor 532 that result in current flowing through the statorwindings that generates a stator magnetic field and causes the rotor ofthe motor 532 to rotate. The motor control module 512 is configured toreceive or otherwise obtain a commanded dosage from the pump controlsystem 520, convert the commanded dosage to a commanded translationaldisplacement of the plunger 517, and command, signal, or otherwiseoperate the motor driver module 514 to cause the rotor of the motor 532to rotate by an amount that produces the commanded translationaldisplacement of the plunger 517. For example, the motor control module512 may determine an amount of rotation of the rotor required to producetranslational displacement of the plunger 517 that achieves thecommanded dosage received from the pump control system 520. Based on thecurrent rotational position (or orientation) of the rotor with respectto the stator that is indicated by the output of the rotor sensingarrangement 516, the motor control module 512 determines the appropriatesequence of alternating electrical signals to be applied to therespective phases of the stator windings that should rotate the rotor bythe determined amount of rotation from its current position (ororientation). In embodiments where the motor 532 is realized as a BLDCmotor, the alternating electrical signals commutate the respectivephases of the stator windings at the appropriate orientation of therotor magnetic poles with respect to the stator and in the appropriateorder to provide a rotating stator magnetic field that rotates the rotorin the desired direction. Thereafter, the motor control module 512operates the motor driver module 514 to apply the determined alternatingelectrical signals (e.g., the command signals) to the stator windings ofthe motor 532 to achieve the desired delivery of fluid to the user.

When the motor control module 512 is operating the motor driver module514, current flows from the energy source 518 through the statorwindings of the motor 532 to produce a stator magnetic field thatinteracts with the rotor magnetic field. In some embodiments, after themotor control module 512 operates the motor driver module 514 and/ormotor 532 to achieve the commanded dosage, the motor control module 512ceases operating the motor driver module 514 and/or motor 532 until asubsequent dosage command is received. In this regard, the motor drivermodule 514 and the motor 532 enter an idle state during which the motordriver module 514 effectively disconnects or isolates the statorwindings of the motor 532 from the energy source 518. In other words,current does not flow from the energy source 518 through the statorwindings of the motor 532 when the motor 532 is idle, and thus, themotor 532 does not consume power from the energy source 518 in the idlestate, thereby improving efficiency.

Depending on the embodiment, the motor control module 512 may beimplemented or realized with a general purpose processor, amicroprocessor, a controller, a microcontroller, a state machine, acontent addressable memory, an application specific integrated circuit,a field programmable gate array, any suitable programmable logic device,discrete gate or transistor logic, discrete hardware components, or anycombination thereof, designed to perform the functions described herein.In exemplary embodiments, the motor control module 512 includes orotherwise accesses a data storage element or memory, including any sortof random access memory (RAM), read only memory (ROM), flash memory,registers, hard disks, removable disks, magnetic or optical massstorage, or any other short or long term storage media or othernon-transitory computer-readable medium, which is capable of storingprogramming instructions for execution by the motor control module 512.The computer-executable programming instructions, when read and executedby the motor control module 512, cause the motor control module 512 toperform or otherwise support the tasks, operations, functions, andprocesses described herein.

It should be appreciated that FIG. 5 is a simplified representation ofthe infusion device 502 for purposes of explanation and is not intendedto limit the subject matter described herein in any way. In this regard,depending on the embodiment, some features and/or functionality of thesensing arrangement 504 may implemented by or otherwise integrated intothe pump control system 520, or vice versa. Similarly, in practice, thefeatures and/or functionality of the motor control module 512 mayimplemented by or otherwise integrated into the pump control system 520,or vice versa. Furthermore, the features and/or functionality of thepump control system 520 may be implemented by control electronics 224located in the fluid infusion device 502, while in alternativeembodiments, the pump control system 520 may be implemented by a remotecomputing device that is physically distinct and/or separate from theinfusion device 502, such as, for example, the CCD 106 or the computingdevice 108.

FIG. 6 depicts an exemplary embodiment of a pump control system 600suitable for use as the pump control system 520 in FIG. 5 in accordancewith one or more embodiments. The illustrated pump control system 600includes, without limitation, a pump control module 602, acommunications interface 604, and a data storage element (or memory)606. The pump control module 602 is coupled to the communicationsinterface 604 and the memory 606, and the pump control module 602 issuitably configured to support the operations, tasks, and/or processesdescribed herein. In various embodiments, the pump control module 602 isalso coupled to one or more user interface elements (e.g., userinterface 230, 540) for receiving user inputs (e.g., target glucosevalues or other glucose thresholds) and providing notifications, alerts,or other therapy information to the user.

The communications interface 604 generally represents the hardware,circuitry, logic, firmware and/or other components of the pump controlsystem 600 that are coupled to the pump control module 602 andconfigured to support communications between the pump control system 600and the various sensing arrangements 504, 506, 508. In this regard, thecommunications interface 604 may include or otherwise be coupled to oneor more transceiver modules capable of supporting wirelesscommunications between the pump control system 520, 600 and the sensingarrangement 504, 506, 508. For example, the communications interface 604may be utilized to receive sensor measurement values or othermeasurement data from each sensing arrangement 504, 506, 508 in aninfusion system 500. In other embodiments, the communications interface604 may be configured to support wired communications to/from thesensing arrangement(s) 504, 506, 508. In various embodiments, thecommunications interface 604 may also support communications withanother electronic device (e.g., CCD 106 and/or computer 108) in aninfusion system (e.g., to upload sensor measurement values to a serveror other computing device, receive control information from a server orother computing device, and the like).

The pump control module 602 generally represents the hardware,circuitry, logic, firmware and/or other component of the pump controlsystem 600 that is coupled to the communications interface 604 andconfigured to determine dosage commands for operating the motor 532 todeliver fluid to the body 501 based on measurement data received fromthe sensing arrangements 504, 506, 508 and perform various additionaltasks, operations, functions and/or operations described herein. Forexample, in exemplary embodiments, pump control module 602 implements orotherwise executes a command generation application 610 that supportsone or more autonomous operating modes and calculates or otherwisedetermines dosage commands for operating the motor 532 of the infusiondevice 502 in an autonomous operating mode based at least in part on acurrent measurement value for a condition in the body 501 of the user.For example, in a closed-loop operating mode, the command generationapplication 610 may determine a dosage command for operating the motor532 to deliver insulin to the body 501 of the user based at least inpart on the current glucose measurement value most recently receivedfrom the sensing arrangement 504 to regulate the user's blood glucoselevel to a target reference glucose value. Additionally, the commandgeneration application 610 may generate dosage commands for boluses thatare manually-initiated or otherwise instructed by a user via a userinterface element.

In exemplary embodiments, the pump control module 602 also implements orotherwise executes a personalization application 608 that iscooperatively configured to interact with the command generationapplication 610 to support adjusting dosage commands or controlinformation dictating the manner in which dosage commands are generatedin a personalized, user-specific (or patient-specific) manner, asdescribed in greater detail below. In this regard, in some embodiments,based on correlations between current or recent measurement data and thecurrent operational context relative to historical data associated withthe patient, the personalization application 608 may adjust or otherwisemodify values for one or more parameters utilized by the commandgeneration application 610 when determining dosage commands, forexample, by modifying a parameter value at a register or location inmemory 606 referenced by the command generation application 610. In yetother embodiments, the personalization application 608 may predict mealsor other events or activities that are likely to be engaged in by theuser and output or otherwise provide an indication of the predicted userbehavior for confirmation or modification by the user, which, in turn,may then be utilized to adjust the manner in which dosage commands aregenerated to regulate glucose in a manner that accounts for the user'sbehavior in a personalized manner.

Still referring to FIG. 6, depending on the embodiment, the pump controlmodule 602 may be implemented or realized with a general purposeprocessor, a microprocessor, a controller, a microcontroller, a statemachine, a content addressable memory, an application specificintegrated circuit, a field programmable gate array, any suitableprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof, designed to perform thefunctions described herein. In this regard, the steps of a method oralgorithm described in connection with the embodiments disclosed hereinmay be embodied directly in hardware, in firmware, in a software moduleexecuted by the pump control module 602, or in any practical combinationthereof. In exemplary embodiments, the pump control module 602 includesor otherwise accesses the data storage element or memory 606, which maybe realized using any sort of non-transitory computer-readable mediumcapable of storing programming instructions for execution by the pumpcontrol module 602. The computer-executable programming instructions,when read and executed by the pump control module 602, cause the pumpcontrol module 602 to implement or otherwise generate the applications608, 610 and perform tasks, operations, functions, and processesdescribed herein.

It should be understood that FIG. 6 is a simplified representation of apump control system 600 for purposes of explanation and is not intendedto limit the subject matter described herein in any way. For example, insome embodiments, the features and/or functionality of the motor controlmodule 512 may be implemented by or otherwise integrated into the pumpcontrol system 600 and/or the pump control module 602, for example, bythe command generation application 610 converting the dosage commandinto a corresponding motor command, in which case, the separate motorcontrol module 512 may be absent from an embodiment of the infusiondevice 502.

FIG. 7 depicts an exemplary closed-loop control system 700 that may beimplemented by a pump control system 520, 600 to provide a closed-loopoperating mode that autonomously regulates a condition in the body of auser to a reference (or target) value. It should be appreciated thatFIG. 7 is a simplified representation of the control system 700 forpurposes of explanation and is not intended to limit the subject matterdescribed herein in any way.

In exemplary embodiments, the control system 700 receives or otherwiseobtains a target glucose value at input 702. In some embodiments, thetarget glucose value may be stored or otherwise maintained by theinfusion device 502 (e.g., in memory 606), however, in some alternativeembodiments, the target value may be received from an external component(e.g., CCD 106 and/or computer 108). In one or more embodiments, thetarget glucose value may be calculated or otherwise determined prior toentering the closed-loop operating mode based on one or morepatient-specific control parameters. For example, the target bloodglucose value may be calculated based at least in part on apatient-specific reference basal rate and a patient-specific dailyinsulin requirement, which are determined based on historical deliveryinformation over a preceding interval of time (e.g., the amount ofinsulin delivered over the preceding 24 hours). The control system 700also receives or otherwise obtains a current glucose measurement value(e.g., the most recently obtained sensor glucose value) from the sensingarrangement 504 at input 704. The illustrated control system 700implements or otherwise provides proportional-integral-derivative (PID)control to determine or otherwise generate delivery commands foroperating the motor 510 based at least in part on the difference betweenthe target glucose value and the current glucose measurement value. Inthis regard, the PID control attempts to minimize the difference betweenthe measured value and the target value, and thereby regulates themeasured value to the desired value. PID control parameters are appliedto the difference between the target glucose level at input 702 and themeasured glucose level at input 704 to generate or otherwise determine adosage (or delivery) command provided at output 730. Based on thatdelivery command, the motor control module 512 operates the motor 510 todeliver insulin to the body of the user to influence the user's glucoselevel, and thereby reduce the difference between a subsequently measuredglucose level and the target glucose level.

The illustrated control system 700 includes or otherwise implements asummation block 706 configured to determine a difference between thetarget value obtained at input 702 and the measured value obtained fromthe sensing arrangement 504 at input 704, for example, by subtractingthe target value from the measured value. The output of the summationblock 706 represents the difference between the measured and targetvalues, which is then provided to each of a proportional term path, anintegral term path, and a derivative term path. The proportional termpath includes a gain block 720 that multiplies the difference by aproportional gain coefficient, K_(P), to obtain the proportional term.The integral term path includes an integration block 708 that integratesthe difference and a gain block 722 that multiplies the integrateddifference by an integral gain coefficient, K_(I), to obtain theintegral term. The derivative term path includes a derivative block 710that determines the derivative of the difference and a gain block 724that multiplies the derivative of the difference by a derivative gaincoefficient, K_(D), to obtain the derivative term. The proportionalterm, the integral term, and the derivative term are then added orotherwise combined to obtain a delivery command that is utilized tooperate the motor at output 730. Various implementation detailspertaining to closed-loop PID control and determining gain coefficientsare described in greater detail in U.S. Pat. No. 7,402,153, which isincorporated by reference.

In one or more exemplary embodiments, the PID gain coefficients areuser-specific (or patient-specific) and dynamically calculated orotherwise determined prior to entering the closed-loop operating modebased on historical insulin delivery information (e.g., amounts and/ortimings of previous dosages, historical correction bolus information, orthe like), historical sensor measurement values, historical referenceblood glucose measurement values, user-reported or user-input events(e.g., meals, exercise, and the like), and the like. In this regard, oneor more patient-specific control parameters (e.g., an insulinsensitivity factor, a daily insulin requirement, an insulin limit, areference basal rate, a reference fasting glucose, an active insulinaction duration, pharmodynamical time constants, or the like) may beutilized to compensate, correct, or otherwise adjust the PID gaincoefficients to account for various operating conditions experiencedand/or exhibited by the infusion device 502. The PID gain coefficientsmay be maintained by the memory 606 accessible to the pump controlmodule 602. In this regard, the memory 606 may include a plurality ofregisters associated with the control parameters for the PID control.For example, a first parameter register may store the target glucosevalue and be accessed by or otherwise coupled to the summation block 706at input 702, and similarly, a second parameter register accessed by theproportional gain block 720 may store the proportional gain coefficient,a third parameter register accessed by the integration gain block 722may store the integration gain coefficient, and a fourth parameterregister accessed by the derivative gain block 724 may store thederivative gain coefficient.

As described in greater detail below, in one or more exemplaryembodiments, one or more parameters of the closed-loop control system700 are automatically adjusted or adapted in a personalized manner toaccount for potential changes in the user's glucose level or insulinsensitivity resulting from meals, exercise, or other events oractivities. For example, in one or more embodiments, the target glucosevalue 702 may be decreased in advance of a predicted meal event toachieve an increase in the insulin infusion rate to effectivelypre-bolus a meal, and thereby reduce the likelihood of postprandialhyperglycemia. Additionally or alternatively, the time constant or gaincoefficient associated with one or more paths of the closed-loop controlsystem 700 may be adjusted to tune the responsiveness to deviationsbetween the measured glucose value 704 and the target glucose value 702.For example, based on the particular type of meal being consumed or theparticular time of day during which the meal is consumed, the timeconstant associated with the derivative block 710 or derivative termpath may be adjusted to make the closed-loop control more or lessaggressive in response to an increase in the user's glucose level basedon the user's historical glycemic response to the particular type ofmeal.

FIG. 8 depicts an exemplary embodiment of a patient monitoring system800. The patient monitoring system 800 includes a medical device 802that is communicatively coupled to a sensing element 804 that isinserted into the body of a patient or otherwise worn by the patient toobtain measurement data indicative of a physiological condition in thebody of the patient, such as a sensed glucose level. The medical device802 is communicatively coupled to a client device 806 via acommunications network 810, with the client device 806 beingcommunicatively coupled to a remote device 814 via anothercommunications network 812. In this regard, the client device 806 mayfunction as an intermediary for uploading or otherwise providingmeasurement data from the medical device 802 to the remote device 814.It should be appreciated that FIG. 8 depicts a simplified representationof a patient monitoring system 800 for purposes of explanation and isnot intended to limit the subject matter described herein in any way.

In exemplary embodiments, the client device 806 is realized as a mobilephone, a smartphone, a tablet computer, or other similar mobileelectronic device; however, in other embodiments, the client device 806may be realized as any sort of electronic device capable ofcommunicating with the medical device 802 via network 810, such as alaptop or notebook computer, a desktop computer, or the like. Inexemplary embodiments, the network 810 is realized as a Bluetoothnetwork, a ZigBee network, or another suitable personal area network.That said, in other embodiments, the network 810 could be realized as awireless ad hoc network, a wireless local area network (WLAN), or localarea network (LAN). The client device 806 includes or is coupled to adisplay device, such as a monitor, screen, or another conventionalelectronic display, capable of graphically presenting data and/orinformation pertaining to the physiological condition of the patient.The client device 806 also includes or is otherwise associated with auser input device, such as a keyboard, a mouse, a touchscreen, or thelike, capable of receiving input data and/or other information from theuser of the client device 806.

In exemplary embodiments, a user, such as the patient, the patient'sdoctor or another healthcare provider, or the like, manipulates theclient device 806 to execute a client application 808 that supportscommunicating with the medical device 802 via the network 810. In thisregard, the client application 808 supports establishing acommunications session with the medical device 802 on the network 810and receiving data and/or information from the medical device 802 viathe communications session. The medical device 802 may similarly executeor otherwise implement a corresponding application or process thatsupports establishing the communications session with the clientapplication 808. The client application 808 generally represents asoftware module or another feature that is generated or otherwiseimplemented by the client device 806 to support the processes describedherein. Accordingly, the client device 806 generally includes aprocessing system and a data storage element (or memory) capable ofstoring programming instructions for execution by the processing system,that, when read and executed, cause processing system to create,generate, or otherwise facilitate the client application 808 and performor otherwise support the processes, tasks, operations, and/or functionsdescribed herein. Depending on the embodiment, the processing system maybe implemented using any suitable processing system and/or device, suchas, for example, one or more processors, central processing units(CPUs), controllers, microprocessors, microcontrollers, processing coresand/or other hardware computing resources configured to support theoperation of the processing system described herein. Similarly, the datastorage element or memory may be realized as a random access memory(RAM), read only memory (ROM), flash memory, magnetic or optical massstorage, or any other suitable non-transitory short or long term datastorage or other computer-readable media, and/or any suitablecombination thereof.

In one or more embodiments, the client device 806 and the medical device802 establish an association (or pairing) with one another over thenetwork 810 to support subsequently establishing a point-to-point orpeer-to-peer communications session between the medical device 802 andthe client device 806 via the network 810. For example, in accordancewith one embodiment, the network 810 is realized as a Bluetooth network,wherein the medical device 802 and the client device 806 are paired withone another (e.g., by obtaining and storing network identificationinformation for one another) by performing a discovery procedure oranother suitable pairing procedure. The pairing information obtainedduring the discovery procedure allows either of the medical device 802or the client device 806 to initiate the establishment of a securecommunications session via the network 810.

In one or more exemplary embodiments, the client application 808 is alsoconfigured to store or otherwise maintain an address and/or otheridentification information for the remote device 814 on the secondnetwork 812. In this regard, the second network 812 may be physicallyand/or logically distinct from the network 810, such as, for example,the Internet, a cellular network, a wide area network (WAN), or thelike. The remote device 814 generally represents a server or othercomputing device configured to receive and analyze or otherwise monitormeasurement data, event log data, and potentially other informationobtained for the patient associated with the medical device 802. Inexemplary embodiments, the remote device 814 is coupled to a database816 configured to store or otherwise maintain data associated withindividual patients. In practice, the remote device 814 may reside at alocation that is physically distinct and/or separate from the medicaldevice 802 and the client device 806, such as, for example, at afacility that is owned and/or operated by or otherwise affiliated with amanufacturer of the medical device 802. For purposes of explanation, butwithout limitation, the remote device 814 may alternatively be referredto herein as a server.

Still referring to FIG. 8, the sensing element 804 generally representsthe component of the patient monitoring system 800 that is configured togenerate, produce, or otherwise output one or more electrical signalsindicative of a physiological condition that is sensed, measured, orotherwise quantified by the sensing element 804. In this regard, thephysiological condition of a user influences a characteristic of theelectrical signal output by the sensing element 804, such that thecharacteristic of the output signal corresponds to or is otherwisecorrelative to the physiological condition that the sensing element 804is sensitive to. In exemplary embodiments, the sensing element 804 isrealized as an interstitial glucose sensing element inserted at alocation on the body of the patient that generates an output electricalsignal having a current (or voltage) associated therewith that iscorrelative to the interstitial fluid glucose level that is sensed orotherwise measured in the body of the patient by the sensing element804.

The medical device 802 generally represents the component of the patientmonitoring system 800 that is communicatively coupled to the output ofthe sensing element 804 to receive or otherwise obtain the measurementdata samples from the sensing element 804 (e.g., the measured glucoseand characteristic impedance values), store or otherwise maintain themeasurement data samples, and upload or otherwise transmit themeasurement data to the server 814 via the client device 806. In one ormore embodiments, the medical device 802 is realized as an infusiondevice 102, 200, 502 configured to deliver a fluid, such as insulin, tothe body of the patient. That said, in other embodiments, the medicaldevice 802 could be a standalone sensing or monitoring device separateand independent from an infusion device (e.g., sensing arrangement 104,504). It should be noted that although FIG. 8 depicts the medical device802 and the sensing element 804 as separate components, in practice, themedical device 802 and the sensing element 804 may be integrated orotherwise combined to provide a unitary device that can be worn by thepatient.

In exemplary embodiments, the medical device 802 includes a controlmodule 822, a data storage element 824 (or memory), and a communicationsinterface 826. The control module 822 generally represents the hardware,circuitry, logic, firmware and/or other component(s) of the medicaldevice 802 that is coupled to the sensing element 804 to receive theelectrical signals output by the sensing element 804 and perform orotherwise support various additional tasks, operations, functions and/orprocesses described herein. Depending on the embodiment, the controlmodule 822 may be implemented or realized with a general purposeprocessor, a microprocessor, a controller, a microcontroller, a statemachine, a content addressable memory, an application specificintegrated circuit, a field programmable gate array, any suitableprogrammable logic device, discrete gate or transistor logic, discretehardware components, or any combination thereof, designed to perform thefunctions described herein. In some embodiments, the control module 822includes an analog-to-digital converter (ADC) or another similarsampling arrangement that samples or otherwise converts an outputelectrical signal received from the sensing element 804 intocorresponding digital measurement data value. In other embodiments, thesensing element 804 may incorporate an ADC and output a digitalmeasurement value.

The communications interface 826 generally represents the hardware,circuitry, logic, firmware and/or other components of the medical device802 that are coupled to the control module 822 for outputting dataand/or information from/to the medical device 802 to/from the clientdevice 806. For example, the communications interface 826 may include orotherwise be coupled to one or more transceiver modules capable ofsupporting wireless communications between the medical device 802 andthe client device 806. In exemplary embodiments, the communicationsinterface 826 is realized as a Bluetooth transceiver or adapterconfigured to support Bluetooth Low Energy (BLE) communications.

In exemplary embodiments, the remote device 814 receives, from theclient device 806, measurement data values associated with a particularpatient (e.g., sensor glucose measurements, acceleration measurements,and the like) that were obtained using the sensing element 804, and theremote device 814 stores or otherwise maintains the historicalmeasurement data in the database 816 in association with the patient(e.g., using one or more unique patient identifiers). Additionally, theremote device 814 may also receive, from or via the client device 806,meal data or other event log data that may be input or otherwiseprovided by the patient (e.g., via client application 808) and store orotherwise maintain historical meal data and other historical event oractivity data associated with the patient in the database 816. In thisregard, the meal data include, for example, a time or timestampassociated with a particular meal event, a meal type or otherinformation indicative of the content or nutritional characteristics ofthe meal, and an indication of the size associated with the meal. Inexemplary embodiments, the remote device 814 also receives historicalfluid delivery data corresponding to basal or bolus dosages of fluiddelivered to the patient by an infusion device 102, 200, 502. Forexample, the client application 808 may communicate with an infusiondevice 102, 200, 502 to obtain insulin delivery dosage amounts andcorresponding timestamps from the infusion device 102, 200, 502, andthen upload the insulin delivery data to the remote device 814 forstorage in association with the particular patient. The remote device814 may also receive geolocation data and potentially other contextualdata associated with a device 802, 806 from the client device 806 and/orclient application 808, and store or otherwise maintain the historicaloperational context data in association with the particular patient. Inthis regard, one or more of the devices 802, 806 may include a globalpositioning system (GPS) receiver or similar modules, components orcircuitry capable of outputting or otherwise providing datacharacterizing the geographic location of the respective device 802, 806in real-time.

The historical patient data may be analyzed by one or more of the remotedevice 814, the client device 806, and/or the medical device 802 toalter or adjust operation of an infusion device 102, 200, 502 toinfluence fluid delivery in a personalize manner. For example, thepatient's historical meal data and corresponding measurement data orother contextual data may be analyzed to predict a future time when thenext meal is likely to be consumed by the patient, the likelihood of afuture meal event within a specific time period, the likely size oramount of carbohydrates associated with a future meal, the likely typeor nutritional content of the future meal, and/or the like. Moreover,the patient's historical measurement data for postprandial periodsfollowing historical meal events may be analyzed to model or otherwisecharacterize the patient's glycemic response to the predicted size andtype of meal for the current context (e.g., time of day, day of week,geolocation, etc.). One or more aspects of the infusion device 102, 200,502 that control or regulate insulin delivery may then be modified oradjusted to proactively account for the patient's likely meal activityand glycemic response.

In one or more exemplary embodiments, the remote device 814 utilizesmachine learning to determine which combination of historical sensorglucose measurement data, historical delivery data, historical auxiliarymeasurement data (e.g., historical acceleration measurement data,historical heart rate measurement data, and/or the like), historicalevent log data, historical geolocation data, and other historical orcontextual data are correlated to or predictive of the occurrence of aparticular event, activity, or metric for a particular patient, and thendetermines a corresponding equation, function, or model for calculatingthe value of the parameter of interest based on that set of inputvariables. Thus, the model is capable of characterizing or mapping aparticular combination of one or more of the current (or recent) sensorglucose measurement data, auxiliary measurement data, delivery data,geographic location, patient behavior or activities, and the like to avalue representative of the current probability or likelihood of aparticular event or activity or a current value for a parameter ofinterest. It should be noted that since each patient's physiologicalresponse may vary from the rest of the population, the subset of inputvariables that are predictive of or correlative for a particular patientmay vary from other users. Additionally, the relative weightings appliedto the respective variables of that predictive subset may also vary fromother patients who may have common predictive subsets, based ondiffering correlations between a particular input variable and thehistorical data for that particular patient. It should be noted that anynumber of different machine learning techniques may be utilized by theremote device 814 to determine what input variables are predictive for acurrent patient of interest, such as, for example, artificial neuralnetworks, genetic programming, support vector machines, Bayesiannetworks, probabilistic machine learning models, or other Bayesiantechniques, fuzzy logic, heuristically derived combinations, or thelike.

Prospective Meal Adjustments

FIG. 9 depicts an exemplary prospective closed-loop control process 900suitable for implementation by an infusion device (or a control systemassociated therewith) to automatically adjust closed-loop controlinformation in advance of a meal or other future event or activity thatis likely to influence a patient's glucose level or insulin response. Inthis regard, the prospective closed-loop control process 900 compensatesfor the relatively slow action of subcutaneously infused insulin byproactively adjusting insulin infusion rates to account for thepatient's likely glycemic response to the future event.

The various tasks performed in connection with the control process 900may be performed by hardware, firmware, software executed by processingcircuitry, or any combination thereof. For illustrative purposes, thefollowing description refers to elements mentioned above in connectionwith FIGS. 1-8. In practice, portions of the control process 900 may beperformed by different elements of an infusion system, such as, forexample, an infusion device 102, 200, 502, 802, a client computingdevice 106, 806, a remote computing device 108, 814, and/or a pumpcontrol system 520, 600. It should be appreciated that the controlprocess 900 may include any number of additional or alternative tasks,the tasks need not be performed in the illustrated order and/or thetasks may be performed concurrently, and/or the control process 900 maybe incorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 9 couldbe omitted from a practical embodiment of the control process 900 aslong as the intended overall functionality remains intact.

The illustrated prospective closed-loop control process 900 begins byreceiving or otherwise obtaining historical meal data for the patientand calculating or otherwise determining a future meal probability basedat least in part on the correlation between the current operationalcontext and the historical operational context associated with past mealevents (tasks 902, 904). For example, one of an infusion device 102,200, 502, 802, a client device 106, 806, or a remote computing device108, 814 may retrieve or otherwise obtain the historical meal dataassociated with the patient from the database 816 and analyze thehistorical meal data to identify previous meal events logged, entered,or otherwise input by the patient or other user of the client device106, 806. Based on the timestamps associated with those previous mealevents and potentially other contextual information pertaining tooperation of the infusion device 102, 200, 502, 802 (e.g., geolocationdata, and/or the like), the probability of the patient consuming a mealat a particular point in time in the future or within a futureprediction window (or horizon) in advance of the current time may becalculated based at least in part on the current operational context(e.g., the current time of day, the current day of the week, currentgeographic location of the infusion device 102, 200, 502, 802 or theclient device 106, 806, and the like).

For example, each day (or 24-hour period) may be divided into a numberof smaller time segments, with past meal events being assigned toparticular individual segments of the day. In one exemplary embodiment,each day is divided into 15 minute segments (or 96 total segments perday). For each 15 minute segment, any meal event timestamped within aprediction horizon (e.g., the next 60 minutes) is assigned to thatsegment. The probability of meal presence within the prediction horizoncan be determined using a conditional probabilistic model by dividingthe number of days where there was a meal event within the predictionhorizon for the current segment by the total number of days of mealhistory that exist. For example, if the current segment corresponds tothe period between 8:00 A.M. and 8:15 A.M., the prediction horizon is 60minutes, and for 7 days out of the 10 days of meal history the patientconsumed a meal within the period between 8:00 A.M. and 9:00 A.M., thefuture meal probability may be estimated to be 70% at or within the timeperiod between 8:00 A.M. and 8:15 A.M.

Additionally, for each segment, the probability of a particular size ortype of meal being consumed within the prediction horizon may also bedetermined by dividing the number of meals assigned to a particular sizeor type by the total number of meals within that prediction horizon. Forexample, for the 7 meals consumed within the period between 8:00 A.M.and 9:00 A.M., 4 meal events may have been indicated as having a smallsize, 2 meal events may have been indicated as having a medium or normalsize, and 1 meal event may have been indicated as having a large size.Accordingly, the probability of a small meal may be estimated as 57%,the probability of a normal meal may be estimated as 29%, and theprobability of a large meal may be estimated as 14%. In a similarmanner, where meal events are logged or tagged with a particular type ofnutritional content, the prospective closed-loop control process 900 maybe configured to probabilistically determine the likely nutritionalcontent for a future meal within the prediction horizon.

It should be noted that the historical meal data utilized to determinethe future meal probabilities may be filtered to account for the currentday of the week, the current geographic location of the patient, orpotentially other factors. For example, continuing the above example,there may be 70 days worth of patient history accounted for by thepatient's historical meal data, but with only 10 of those dayscorresponding to the current day of the week. The historical meal datafor those 10 days may then be utilized to determine meal probabilitiesfor the current day of the week as described above.

In one or more exemplary embodiments, the remote device 814 maydetermine meal probabilities or predicted future meal times for thepatient and provide indication of the calculated meal probabilities tothe infusion device 802 or the client device 806. However, inalternative embodiments, the personalization application 608 at theinfusion device 802 or the client application 808 at the client device806 may request or retrieve historical meal data for the current contextfrom the database 816 via the remote device 814 and determine mealprobabilities or predicted future meal times for the patientsubstantially in real-time at the respective device 802, 806.

In another embodiment, machine learning is utilized to determinepersonalized models for meal probability (or probability of mealoccurrence) and meal size, which, in turn, may be utilized to calculatemeal probability and probable meal size substantially in real-time as afunction of current measurement data and other current operationalcontext information. For example, the remote device 814 may analyze thehistorical meal data, historical sensor glucose measurement data,historical insulin delivery data, historical auxiliary measurement data,historical geographic location data, and any other historical dataassociated with the patient in the database 816 to identify or otherwisedetermine the subset of the patient's historical data that is predictiveof or correlative to meal occurrence. The remote device 814 may thendetermine a corresponding equation for calculating a meal probabilityvalue based on that subset of input variables, thereby characterizing ormapping a particular combination of values or attributes for the currentoperational context to a corresponding meal probability. In one or moreembodiments, different meal probability models are determined fordifferent times of the day (e.g., for a breakfast period to be utilizedbetween 6 A.M. and 10 A.M., a lunch period to be utilized between 10A.M. and 2 P.M., and so on), different days of the week (e.g., a weekdaymodel for the breakfast period versus a weekend model for the breakfastperiod), different geographic locations, or other operational contexts.Similarly, the remote device 814 may analyze the historical meal data,historical sensor glucose measurement data, historical insulin deliverydata, historical auxiliary measurement data, historical geographiclocation data, and any other historical data associated with the patientin the database 816 to identify or otherwise determine the subset of thepatient's historical data that is predictive of or correlative to aparticular meal size, and then determine a corresponding equation forcalculating a probable carbohydrate value based on that subset of inputvariables. Such machine learning models may be dynamically determined orupdated on a periodic basis (e.g., daily, weekly, monthly, or the like)to reflect changes or trends in the patient's behavior. In variousembodiments, the remote device 814 automatically pushes updated modelsto the client application 808 or the personalization application 608,which, in turn utilize the models to continually calculate predictedmeal probabilities and corresponding meal sizes substantially inreal-time as new sensor glucose measurement data or other auxiliarymeasurement data is available, as the geographic location of therespective device 802, 806 changes, the time of day changes, and so on.

Still referring to FIG. 9, in response to determining the predictedfuture meal probability is greater than a threshold, the prospectiveclosed-loop control process 900 automatically adjusts or modifiescontrol information associated with the closed-loop control scheme toproactively increase insulin delivery in anticipation of a future mealin advance of receiving any meal indication from the patient (tasks 906,908). For example, in one or more embodiments, the pump control system520, 600 may automatically reduce the target glucose value 702 for atemporary duration of time corresponding to the prediction horizon whenthe predicted future meal probability within the prediction horizon isgreater than a threshold value (e.g., greater than 50%). In someembodiments, the amount by which the target glucose value 702 is reducedis dependent on or influenced by the probable meal size. For example, ifthe most probable meal size is classified or characterized as a smallmeal, the target glucose value 702 may be reduced from 120 milligramsper deciliter (mg/dL) to 100 mg/dL, while the target glucose value 702may be reduced to 85 mg/dL when a normal or medium meal is most probableand to 70 mg/dL when a large meal is most probable. Additionally oralternatively, the pump control system 520, 600 may automatically adjustone or more other control parameters or settings associated with thecontrol scheme, such as, for example, increasing the minimum or maximumbasal delivery rates. In one or more embodiments, the pump controlsystem 520, 600 may automatically operate the motor 532 to deliver acorrection bolus in advance of receiving any meal indication.

In one or more embodiments, the type or magnitude of the automatedadjustments performed by the pump control system 520, 600 are influencedby the future meal probability and/or the probable future meal size orcontent. For example, when the probable future meal size within theprediction horizon corresponds to a large meal with a relatively highmeal probability (e.g., greater than 75% probability of a large mealwithin the prediction horizon), the pump control system 520, 600 mayautomatically reduce the target glucose value 702 and increase one ormore minimum or maximum basal delivery rate settings while alsodetermining a correction bolus dosage based on the predicted meal sizeto mitigate the likelihood of a postprandial hyperglycemic excursion.Conversely, for a probable future meal size within the predictionhorizon corresponding to a small meal with a relatively lower mealprobability (e.g., between 50% and 75% meal probability), the pumpcontrol system 520, 600 may automatically reduce the target glucosevalue 702 without modifying basal delivery rate settings oradministering a correction bolus to reduce the likelihood of ahypoglycemic excursion in the event a meal is not consumed.

It should be noted that in some embodiments, the predicted meal size maybe determined as a weighted average of the estimated carbohydrateamounts associated with the different meal sizes. For example,continuing the above example, if the small meal size corresponds to 15grams of carbohydrates, the normal meal size corresponds to 30 grams ofcarbohydrates, and the large meal size corresponds to 50 grams for theparticular patient, the predicted or probable meal size may bedetermined as a weighted sum of the patient-specific meal sizes with therespective probabilities (e.g., 57%×15+29%×30+14%×50=24.3carbohydrates). Thus, the amount of adjustment to the closed-loopcontrol information may correspond to the weighted average of thepatient-specific meal sizes according to their likely probabilities.

In one or more embodiments, after a period of time has elapsed withoutreceiving a meal indication, the prospective closed-loop control process900 may automatically revert to the original or normal closed-loopcontrol information. For example, once the prediction horizon durationof time has elapsed since adjusting the target glucose value 702, thepump control system 520, 600 may automatically restore the targetglucose value 702 to its original reference value (e.g., 120 mg/dL). Inother embodiments, the prospective closed-loop control process 900 isconfigured to continually analyze the predicted meal probability andmaintain the adjusted closed-loop control information until thepredicted meal probability for the current segment falls below thethreshold for triggering adjustment at 906. For example, every 15minutes, the prospective closed-loop control process 900 may determinean updated predicted meal probability for the current segment, andmaintain the adjusted closed-loop control information until thepredicted meal probability falls back below the threshold (e.g., below50%).

Some embodiments of the prospective closed-loop control process 900 maydetermine a predicted meal time for a future meal based on the patient'shistorical meal distribution. For example, when the meal probabilitybased on a number of consecutive or contiguous segments is greater thanthe threshold, the timestamps associated with individual historical mealevents assigned to those relevant segments of the day may be averaged orotherwise analyzed to determine a predicted future meal time. In thisregard, the patient's historical meal data may be analyzed to identify asubset of time segments or windows having the highest mealprobabilities, which may then be utilized to calculate or otherwisedetermine predicted times for when the patient is likely to eatbreakfast, lunch, or dinner in the future. More recent meal events maybe weighted more heavily than older meal events to adaptively adjust thepredicted meal times over time as the patient's behavior changes. Theprospective closed-loop control process 900 may then automaticallyadjust or modify control information associated with the closed-loopcontrol scheme based on the predicted future meal time. For example, thepump control system 520, 600 may identify when the current time iswithin a threshold amount of time in advance of a predicted future mealtime (e.g., one hour before a predicted meal time), and thenautomatically initiate adjustments of the control information at thattime. It should be noted that there are numerous different ways to modela patient's meal behavior and predict when a patient is likely to eat ameal with a given amount of accuracy or reliability, and the subjectmatter describe herein is not necessarily intended to be limited to anyparticular manner or method for predicting whether or when a patient islikely to consume a future meal.

In one or more embodiments, the closed-loop control system 700 ismodified to include an additional input corresponding to the mealprobability corresponding to the current instant in time, that is, theprobability of a meal event being consumed within the forecast window inadvance of the current time. In such embodiments, the future mealprobability may be utilized to dynamically adjust the target glucosevalue 702 as the future meal probability fluctuates (e.g., bycalculating an adjusted target value input to the summation block 706 asa function of the target glucose value 702 and the current mealprobability).

As yet another example, when personalized machine learning models areutilized the automated control adjustments may be dynamically initiatedor terminated in real-time based on real-time changes to measurementdata or other inputs to the models. For example, each time a newmeasurement data sample is received by the infusion device 102, 200,502, 802 from a sensing device 104, 504, 506, 508, 804, thepersonalization application 608 may automatically determine an updatedmeal probability value using the patient-specific meal probability modelcorresponding to the current time of day for the current day of the weekand the current geographic location of the infusion device 102, 200,502, 802. Once the calculated meal probability value based on thecurrent operational context is greater than an adjustment threshold, thepersonalization application 608 may automatically initiate adjustment tothe closed-loop control system 700 implemented by the command generationapplication 610. The personalization application 608 may continually anddynamically determine updated meal probability values as new measurementdata samples are continued to be received until the updated mealprobability value falls below a reversion threshold (which could be thesame as or different from the adjustment threshold for hysteresis), atwhich point the personalization application 608 automatically undoesadjustment to the closed-loop control system 700 to revert to theoriginal or previous configuration.

In some embodiments, the personalization application 608 may dynamicallyvary the adjustments to the closed-loop control system 700 to reflectreal-time fluctuations in the probable meal size. For example, once themeal probability is greater than an adjustment threshold, thepersonalization application 608 may automatically determine a probablemeal size using the patient-specific meal size model corresponding tothe current time of day for the current day of the week and the currentgeographic location of the infusion device 102, 200, 502, 802 andautomatically adjust the closed-loop control system 700 according to theprobable meal size. Thereafter, the personalization application 608 maydynamically determine updated probable meal sizes as new measurementdata samples are continued to be received, and dynamically adjust theclosed-loop control system 700 to reflect changes in the probable mealsize. For example, as the probable meal size dynamically increases, thepersonalization application 608 may progressively reduce the targetglucose value 702 in a manner commensurate to the probable meal size.

It should be noted that the prospective closed-loop control process 900may improve postprandial glucose management by effectively pre-bolusingor pre-loading insulin prior to consumption of a meal to overcome therelatively slow action of subcutaneously administered insulin. Thepatient burden of carbohydrate counting or manually remembering toadminister a bolus or increase insulin delivery before a meal, which maybe particular advantageous in instances where the patient consumes ameal relatively soon after waking (e.g., breakfast) or engaging in someother activity where manually preparing for a meal is inconvenient orcumbersome.

Event Pattern Control Process

FIG. 10 depicts an exemplary event pattern control process 1000 suitablefor implementation by an infusion device (or a control system associatedtherewith) to automatically detect and respond to an event or activitythat is likely to influence an individual's glucose level or insulinresponse, such as, for example, a meal event, an exercise event, astress event, or the like. In this regard, the event pattern controlprocess 1000 detects or otherwise identifies the likely occurrence of anevent and prompts the patient accordingly, thereby alleviating some ofthe burden of manually logging the event and manually configuring theinfusion device to respond to the event.

The various tasks performed in connection with the control process 1000may be performed by hardware, firmware, software executed by processingcircuitry, or any combination thereof. For illustrative purposes, thefollowing description refers to elements mentioned above in connectionwith FIGS. 1-8. In practice, portions of the control process 1000 may beperformed by different elements of an infusion system, such as, forexample, an infusion device 102, 200, 502, 802, a client computingdevice 106, 806, a remote computing device 108, 814, and/or a pumpcontrol system 520, 600. It should be appreciated that the controlprocess 1000 may include any number of additional or alternative tasks,the tasks need not be performed in the illustrated order and/or thetasks may be performed concurrently, and/or the control process 1000 maybe incorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 10 couldbe omitted from a practical embodiment of the control process 1000 aslong as the intended overall functionality remains intact.

The event pattern control process 1000 initializes or begins byreceiving or otherwise obtaining historical event data associated withthe patient and historical measurement data associated with the patientthat corresponds to the historical event data (tasks 1002, 1004). Theillustrated event pattern control process 1000 then creates or otherwisegenerates a patient-specific model for detecting the event as a functionof the patient's measurement data based on the relationship between thehistorical event data and the corresponding historical measurement data(task 1006). In this regard, for an event to be modeled, one of aninfusion device 102, 200, 502, 802, a client device 106, 806, or aremote computing device 108, 814 may retrieve or otherwise obtain thehistorical data for that event that is associated with the patient fromthe database 816 and analyze the historical event data to identifyprevious events logged, entered, or otherwise input by the patient orother user of the client device 106, 806. Based on the timestampsassociated with those previous events, the historical measurement dataassociated with the patient that is concurrent to, contemporaneous to,or surrounding the individual historical events is also obtained fromthe database 816. In a similar manner as described above, for eachhistorical event to be detected, the patient's historical sensor glucosemeasurement data and any other historical auxiliary measurement data(e.g., accelerometer data, heart rate measurement data, geolocationdata, or the like), historical delivery data, and/or other historicalpatient data at or around the time of the event is obtained foranalyzing the relationship or correlation between the historical eventdata and the historical patient's measurement data to identify orotherwise determine the subset of the variables that is predictive of orcorrelative to the occurrence or other attributes of the event to bedetected. A corresponding equation, function, or model may then becreated for calculating a probability of occurrence or other value ormetric for the event based on that correlative subset of input variablesto thereby map the operational context to a corresponding probability orattribute of the event.

For example, for each meal event, the patient's sensor glucosemeasurement data for a preprandial period (e.g., one hour preceding themeal) and a postprandial period (e.g., one hour following the meal) maybe selected for analyzing the relationship between the patient's sensorglucose measurement data and the patient consuming a meal. As anotherexample, for each exercise event, the patient's sensor glucosemeasurement data and accelerometer measurement data for the duration ofthe exercise event along may be selected for analyzing the relationshipbetween the patient's measurement data and the patient engaging inexercise. Additionally, sensor glucose measurement data preceding orfollowing the exercise event may also be selected for modeling thepatient's glycemic response to exercise.

Based on the relationship or correlation between the patient'shistorical measurement data and the patient's historical event data, theinfusion device 102, 200, 502, 802, the client device 106, 806, or theremote computing device 108, 814 may create, develop, or otherwisegenerate a model for determining the probability or likelihood of thepatient experiencing that event as a function of the patient'smeasurement data. In one or more embodiments, the patient's historicalmeasurement data may be utilized to train a neural network or otherartificial intelligence or machine learning model and develop a functionfor predicting the likelihood of occurrence of a particular event as afunction of the patient's measurement data. For example, a model may bedeveloped that predicts the likelihood of a patient having consumed ameal as a function of the patient's sensor glucose measurement data, thepatient's heart rate measurement data, the patient's accelerationmeasurement data, and/or the patient's geolocation data. Similarly, asanother example, a model may be developed that predicts the likelihoodof a patient having engaged in exercise as a function of the patient'ssensor glucose measurement data, the patient's heart rate measurementdata, the patient's acceleration measurement data, and/or the patient'sgeolocation data. In various embodiments, models may be developed topredict attributes of a particular event, such as, for example, mealsize, meal content, exercise intensity, and the like.

Still referring to FIG. 10, in exemplary embodiments, the event patterncontrol process 1000 continues by receiving or otherwise obtainingcurrent measurement data for the patient and detecting or otherwiseidentifying the occurrence of an event based on the relationship orcorrelation between the patient's current measurement data and thepatient's historical measurement data associated with occurrences ofthat type of event (tasks 1008, 1010). In this regard, in a similarmanner as described above in the context of the meal probability modelwith respect to FIG. 9, the infusion device 102, 200, 502, 802 or theclient device 106, 806 may obtain current measurement data values fromthe available sensing devices 104, 504, 506, 508, 804 and input thecurrent measurement data values into the event prediction model tocalculate a probability or likelihood of a particular event. In someembodiments, depending on the amount of measurement data to be inputinto the event prediction model, the infusion device 102, 200, 502, 802or the client device 106, 806 may also buffer recent measurement datavalues preceding the current measurement data values (e.g., thepreceding hour of measurement data) to facilitate detection of an eventbased on a trend or pattern in the recent measurement data. The eventpattern control process 1000 may continually update the calculatedprobability for the event(s) being monitored for using the respectivemodel(s) and detect or otherwise identify the occurrence of an eventpattern in the patient's current or recent measurement data when thecalculated probability for an event is greater than a thresholdpercentage or probability (e.g., greater than 75%).

In one or more embodiments, in response to detecting an event pattern,the application 608, 808 on the infusion device 102, 200, 502, 802 orthe client device 106, 806 is configured to automatically adjustoperation of the infusion device 102, 200, 502, 802 to account for theidentified event. For example, in response to detecting a meal event,the application 608, 808 may automatically calculate a meal bolus amountbased on a predicted meal size and/or a predicted meal content, andautomatically command, signal, or otherwise instruct the commandgeneration application 610 to operate the motor 532 to automaticallydeliver the bolus amount calculated based on the predicted meal sizeand/or predicted meal content. As another example, in response todetecting an exercise event, the application 608, 808 may automaticallydetermine an adjusted target glucose value for the patient based on thetype or intensity of exercise and the patient's historical glycemicresponse to that type of exercise event, and then automatically command,signal, or otherwise instruct the command generation application 610 totemporarily adjust the target glucose value 702 to the adjusted valuethat accounts for the patient's historical glycemic response to thattype of exercise.

In the illustrated embodiment, prior to adjusting operation of theinfusion device, the illustrated control process 1000 generates orotherwise provides a user notification that prompts the patient toconfirm or otherwise verify the occurrence of the identified eventbefore adjusting or otherwise modifying operation of the infusion deviceto account for the identified event based on the user response (tasks1012, 1014). In this regard, an application 608, 808 on the infusiondevice 102, 200, 502, 802 or the client device 106, 806 may generate orotherwise provide a GUI display that indicates the type of event patternthat was detected. For example, the GUI display may indicate that a mealevent was detected based on the patient's measurement data, and in someembodiments, the GUI display may include indication of the predictedmeal size, the predicted meal content, and potentially other attributesof the meal predicted by the application 608, 808. In this regard, insome embodiments, the GUI display may also indicate a recommended actionfor responding to the detected event, such as, for example, a meal bolusamount, an adjusted target glucose value, or another adjustment to thepatient's therapy that was calculated or otherwise recommended by theapplication 608, 808 based on the predicted event. Similarly, for anexercise event, the GUI display may include indication of the detectedexercise intensity (e.g., anaerobic, aerobic, or the like) or thedetected type of exercise, along with a corresponding therapy adjustmentcalculated or otherwise recommended by the application 608, 808 based onthe type of exercise.

In exemplary embodiments, the GUI display includes a button or similarselectable GUI element that may be manipulated by the patient to confirmoccurrence of the detected event and initiate the correspondingadjustment(s) to operation of the infusion device 102, 200, 502, 802.When the detected event is confirmed, a corresponding indication of anew event may be uploaded or otherwise provided to the remote device108, 814 for uploading the patient's historical data to store dataassociated with the event along with the patient's recent measurementdata for storing in association with the new event. The uploaded eventand measurement data may then be utilized to dynamically update theevent prediction model prior to the next iteration of the event patterncontrol process 1000. Alternatively, the GUI display may include GUIelements that allow the patient to invalidate the detected event oroverride or modify the therapy adjustments recommended by theapplication 608, 808. In this regard, when the event is confirmed butthe therapy adjustments are modified, the patient's therapymodifications may be uploaded to the remote device 108, 814 along withthe new event data and corresponding measurement data for dynamicallyupdating or retraining any models utilized to generate therapyrecommendations.

It should be noted that the event pattern control process 1000 mayreduce or alleviate the patient burden of accounting for meals or otherlifestyle events that may impact the patient's glucose level or insulinresponse, while also accounting for instances where the patient mayotherwise delay or forget to adjust operation of the infusion device102, 200, 502, 802 to account for the event. For example, when a mealevent pattern is accurately detected, the patient may be prompted with anotification that identifies the predicted meal attributes andcorresponding meal bolus amount, which the patient may confirm withouthaving to perform carbohydrate counting or other calculations.Similarly, when an exercise event pattern is accurately detected, thepatient may be prompted with a notification that identifies thepredicted type of exercise and corresponding therapy adjustments, whichthe patient may confirm without having to manually characterize theexercise intensity or analyze how operation of the infusion device 102,200, 502, 802 should be altered to account for the patient's estimationof his or her exercise intensity.

Moreover, it should be noted that the event pattern control process 1000may be performed in concert with the prospective closed-loop controlprocess 900 of FIG. 9 to improve the patient's glucose management. Forexample, prior to a meal event, the prospective closed-loop controlprocess 900 may be performed to automatically increase the insulindelivery rate or insulin on board in advance of the meal, with the eventpattern control process 1000 subsequently detecting the occurrence of ameal. Depending on the embodiment, the event pattern control process1000 may then automatically administer a meal bolus for the predictedmeal size and/or content, or alternatively, prompt the patient toconfirm the meal and corresponding bolus. Additionally, theidentification of a meal event by the event pattern control process 1000may also be utilized to trigger or signal the prospective closed-loopcontrol process 900 to revert to the original closed-loop controlinformation once the meal bolus is confirmed and administered. Thus,when the meal is accurately predicted, the prospective closed-loopcontrol process 900 and the event pattern control process 1000 cooperateto improve regulation of the patient's glucose and reduce the burden onthe patient to count carbohydrates or manually configure the infusiondevice 102, 200, 502, 802 to administer a meal bolus or otherwise adjustoperation. In a similar manner, the prospective closed-loop controlprocess 900 and the event pattern control process 1000 may be utilizedin concert to account for exercise, work, school, stress, or otheractivities or events that may influence the patient's insulin responseor glucose level while also reducing the burden on the patient.

Personalized Bolus Process

FIG. 11 depicts an exemplary personalized bolus process 1100 suitablefor implementation by an infusion device (or a control system associatedtherewith) to determine a bolus amount in a personalized manner thatreduces the burden associated with carbohydrate counting. In thisregard, personalized bolus process 1100 allows for the patient toqualitatively define the size, content, or other aspects of a meal, withthe qualitative user input being converted into a correspondingquantitative representation based on the patient's historical data. Thequantitative representation is then utilized to determine a meal bolusdosage without requiring carbohydrate counting or other manualcalculations or estimations.

The various tasks performed in connection with the personalized bolusprocess 1100 may be performed by hardware, firmware, software executedby processing circuitry, or any combination thereof. For illustrativepurposes, the following description refers to elements mentioned abovein connection with FIGS. 1-8. In practice, portions of the personalizedbolus process 1100 may be performed by different elements of an infusionsystem, such as, for example, an infusion device 102, 200, 502, 802, aclient computing device 106, 806, a remote computing device 108, 814,and/or a pump control system 520, 600. It should be appreciated that thepersonalized bolus process 1100 may include any number of additional oralternative tasks, the tasks need not be performed in the illustratedorder and/or the tasks may be performed concurrently, and/or thepersonalized bolus process 1100 may be incorporated into a morecomprehensive procedure or process having additional functionality notdescribed in detail herein. Moreover, one or more of the tasks shown anddescribed in the context of FIG. 11 could be omitted from a practicalembodiment of the personalized bolus process 1100 as long as theintended overall functionality remains intact.

The illustrated personalized bolus process 1100 allows a patient todefine meal sizes qualitatively, such as, for example, small, medium,large, extra large, and/or the like. In exemplary embodiments,personalized bolus process 1100 is implemented once sufficienthistorical meal data and corresponding patient measurement data has beenobtained and stored in the database 816. For example, for an initialsetup monitoring period, the patient may estimate or input meal sizeswhen logging meal events while also manually interacting with a boluswizard or other feature of the infusion device 102, 200, 502, 802 or aclient application 808 on the client device 106, 806 to configure andadminister boluses for the contemporaneous meal events. In this regard,during the initial setup monitoring period, the patient may define ordesignate meals with a particular qualitative meal size while alsoperforming carbohydrate counting and providing indication of theestimated carbohydrate amounts associated with those meals. Once theelapsed duration of the monitoring period is greater than a thresholdsetup period (e.g., 2 weeks) or a sufficient number of meal events havebeen logged or otherwise documented, the personalized bolus process 1100may be enabled.

The personalized bolus process 1100 begins by receiving or otherwiseobtaining an indication of a meal size for the meal to be bolused (task1102). In one or more exemplary embodiments, the personalized bolusprocess 1100 is initiated when the patient interacts with a bolus wizardfeature of a particular application 608, 610, 808 used to administermeal boluses. For example, the client application 808 at the clientdevice 806 may generate or otherwise provide a bolus wizard GUI displaythat includes selectable GUI elements corresponding to differentqualitative meal sizes, with the patient manipulating the client device806 to select the meal size to be assigned to the current meal. In yetother embodiments, the personalized bolus process 1100 is automaticallyinitiated in response to detecting a meal event pattern (e.g., task1010) to calculate a meal bolus dosage based on the predicted meal,where the input meal size corresponds to the predicted meal size basedon the patient's historical meal data, as described above.

In exemplary embodiments, the personalized bolus process 1100 receivesor otherwise obtains the patient's historical meal data for historicalmeal events assigned or associated with the input meal size along withhistorical measurement data and historical insulin delivery datacontemporaneous or concurrent to those historical meal events having theinput meal size (tasks 1104, 1106). Based on the relationship betweenthe historical meal data associated with the previous meal events andthe corresponding historical patient measurement and insulin deliverydata, the personalized bolus process 1100 calculates or otherwisedetermines a patient-specific carbohydrate ratio associated with theinput meal size and a patient-specific carbohydrate amount associatedwith the input meal size (tasks 1108, 1110). In this regard, thepatient-specific carbohydrate ratio accounts for variability of theactual meal size and other factors (e.g., time of day, day of the weak,nutritional components and consumption order for the meal, etc.) thataffect the rate of glucose appearance. Similarly, the estimatedcarbohydrate amount associated with the input meal size may account forvariations in the meal size based on the time of day, the day of theweek, the geolocation, and other factors.

For example, in one or more embodiments, a mathematical model of thepatient's postprandial glucose response to meals having the input mealsize is created using the patient's historical sensor glucosemeasurement values for postprandial periods following the respectivemeal events, historical meal bolus dosages of insulin associated withthe respective meal events, and historical closed-loop or basal insulindeliveries for postprandial and/or preprandial periods surrounding therespective meal events. An average or nominal glucose rate of appearancefor the input meal size may be determined based on the historical sensorglucose measurement values and utilized to determine an estimatedpostprandial peak in the patient's sensor glucose value following a mealevent. The mathematical model may then be optimized to identify anestimated carbohydrate ratio for the input meal size that results in anaverage estimated postprandial peak sensor glucose value at theestimated postprandial peak time following a meal event equal to adesired target postprandial peak sensor glucose value (e.g., 180 mg/dL).In other embodiments, a heuristic statistical analysis may be performedon the patient's historical meal, delivery, and measurement data toidentify a carbohydrate ratio for the input meal size that is likely toachieve a desired postprandial glucose response. It should be noted thatin some embodiments, the historical data sets utilized to determine thecarbohydrate ratio may be further filtered or limited to becontext-specific, for example, to particular periods of the day (e.g.,meal events corresponding to a morning period between 6:00 A.M. and12:00 P.M.), a particular day of the week, a particular geographiclocation, and/or the like.

In one or more exemplary embodiments, the mathematical model of thepatient's postprandial glucose response to meals having the input mealsize can describe the glucose response to insulin delivery and mealconsumption by a set of ordinary differential equations. These equationsmay be based on a mass balance between estimated glucose utilization asresult of insulin delivery and glucose increase as result oftransformation of the meal into blood glucose. The mathematical modelmay also include specific parameters that enable it to predict the bloodglucose at fasting. The mathematical model of the patient's specificmeal response may be adjusted using curve fitting, for example, byadjusting the meal absorption rates in the mathematical model to fit themeasured historical glucose curve and thereby establish the most propermeal absorption rates.

In one or more embodiments, to determine a patient-specific carbohydrateamount associated with the input meal size, machine learning or anotherartificial intelligence technique is utilized to probabilistically modelthe estimated carbohydrates associated with an input meal size as afunction of the time of the day, the day of the week, and potentiallyother context or factors (e.g., the current geographic location, thecurrent sensor glucose measurement value, or the like) using thepatient's historical meal data and corresponding context information,historical measurement data, and/or historical delivery data. Forexample, an equation for calculating a probable carbohydrate value as afunction of the input meal size and other correlative or predictivevariables may be determined and utilized to map the patient's input mealsize to a probable carbohydrate amount given the current operationalcontext. In this manner, the personalized bolus process 1100 accountsfor patient-specific variations in the manner in which the patientqualitatively assesses meal size. For example, based on the datacollected during the initial setup monitoring period, if a patient tendsto characterize a meal having roughly the same amount of carbohydratesas different sizes depending on the time of the day or the day of theweek, the patient-specific quantitative meal size model may beconfigured to increase or decrease the output carbohydrate estimationaccordingly.

In yet other embodiments, a patient-specific carbohydrate amountassociated with the input meal size may be determined heuristicallybased on a statistical analysis of the carbohydrate amounts associatedwith the historical meal events having the input meal size. For example,the estimated carbohydrate amount associated with the input meal sizemay be determined as the average of the carbohydrate amounts associatedwith the input meal size during the initial setup monitoring period. Asanother example, the estimated carbohydrate amount associated with theinput meal size may be determined probabilistically based on thedistribution of the input carbohydrate amounts associated with the inputmeal size between the minimum input carbohydrate amount and the maximuminput carbohydrate amount.

Still referring to FIG. 11, once a carbohydrate ratio and estimatedcarbohydrate amount associated with the input meal size for the currentoperating context are determined, the personalized bolus process 1100continues by calculating or otherwise determining a meal bolus dosageamount using the carbohydrate ratio and the estimated carbohydrateamount and operating the infusion device to administer the meal bolusdosage amount (tasks 1112, 1114). In this regard, the estimatedcarbohydrate amount for the input meal size is multiplied by thecarbohydrate ratio for the input meal size to obtain a correspondingbolus dosage to be administered. The command generation application 610is then commanded, signaled, or otherwise instructed to operate themotor 532 of the infusion device 502 to deliver the calculated bolusdosage of insulin. In some embodiments, the calculated meal bolus dosagemay be automatically administered; however, in other embodiments, anotification of the calculated meal bolus dosage may be generated orotherwise provided on a GUI display for review, modification, and/orconfirmation by the patient. Such a GUI display may also includeindication of the estimated carbohydrate ratio and estimatedcarbohydrate amount determined by the personalized bolus process 1100for review, modification, and/or confirmation. In this regard, someembodiments may allow the patient to override the personalized bolusprocess 1100 and modify one or more of the carbohydrate ratio, thecarbohydrate amount, or the bolus dosage amount. In such scenarios, thepatient modifications may be utilized to update or otherwise adjust themodels for estimating the patient's carbohydrate ratio and/orcarbohydrate amount to be associated with the input meal size forsubsequent iterations of the personalized bolus process 1100.

By virtue of the carbohydrate ratio and the estimated carbohydrateamounts being personalized and context-specific based on the patient'shistorical meal, delivery, and measurement data, the meal bolus accountsfor variations in the rate of glucose appearance for the patient alongwith variations in the actual quantitative meal size relative to theinput qualitative meal size. This allows the patient to merely input aqualitative meal size rather than having to resort to carbohydratecounting while still providing a meal bolus dosage that maintains safeand effective postprandial glucose management.

It should be noted that the personalized bolus process 1100 may beperformed in concert with the prospective closed-loop control process900 and the event pattern control process 1000 to improve the patient'sglucose management. In this regard, as described above, the prospectiveclosed-loop control process 900 automatically increases the insulindelivery rate or insulin on board in advance of the meal. Thepersonalized bolus process 1100 may then be initiated automatically atthe predicted meal time or in response to the event pattern controlprocess 1000 detecting the occurrence of a meal. The personalized bolusprocess 1100 then determines a personalized, context-specific meal bolusdosage corresponding to the meal size that was automatically identified,detected, or predicted. The patient may then simply confirm the detectedmeal size and trigger administration of the meal bolus amount with aslittle as a single user input without any carbohydrate counting or othermanual interaction. Additionally, by virtue of the prospectiveclosed-loop adjustments and the carbohydrate ratio that is personalizedand specific to the current meal size and operational context,variations in the rate of glucose appearance, non-homogeneity of meals,and other factors can be accounted for or otherwise mitigated to improveefficacy of postprandial glucose management.

Personalized Bolusing Using Nutritional Content

FIG. 12 depicts an exemplary nutritional bolus process 1200 suitable forimplementation by an infusion device (or a control system associatedtherewith) to determine a bolus amount based on the nutritional contentof a meal in a personalized manner that reduces the burden associatedwith carbohydrate counting. In this regard, nutritional bolus process1200 allows for the patient to define the nutritional content or type ofmeal being consumed, with a corresponding bolus amount being determinedbased on nutritional characteristics of the meal and the patient'shistorical data. The various tasks performed in connection with thenutritional bolus process 1200 may be performed by hardware, firmware,software executed by processing circuitry, or any combination thereof.For illustrative purposes, the following description refers to elementsmentioned above in connection with FIGS. 1-8. In practice, portions ofthe nutritional bolus process 1200 may be performed by differentelements of an infusion system, such as, for example, an infusion device102, 200, 502, 802, a client computing device 106, 806, a remotecomputing device 108, 814, and/or a pump control system 520, 600. Itshould be appreciated that the nutritional bolus process 1200 mayinclude any number of additional or alternative tasks, the tasks neednot be performed in the illustrated order and/or the tasks may beperformed concurrently, and/or the nutritional bolus process 1200 may beincorporated into a more comprehensive procedure or process havingadditional functionality not described in detail herein. Moreover, oneor more of the tasks shown and described in the context of FIG. 12 couldbe omitted from a practical embodiment of the nutritional bolus process1200 as long as the intended overall functionality remains intact.

Similar to the personalized bolus process 1100, the nutritional bolusprocess 1200 may be manually initiated when the patient interacts with abolus wizard feature of a particular application 608, 610, 808 used toadminister meal boluses, or automatically initiated in response todetecting a meal event pattern. The illustrated nutritional bolusprocess 1200 receivers or otherwise obtains the patient's historicalmeal data along with historical measurement data and historical insulindelivery data contemporaneous or concurrent to historical meal events(tasks 1202, 1204). Based on the relationship between the historicalmeal data associated with the previous meal events and the correspondingcontext associated with the historical meal events, the nutritionalbolus process 1200 calculates or otherwise determines the probable mealcontent given the current operational context (task 1206). In thisregard, the nutritional bolus process 1200 probabilistically predictswhat the patient is likely to be consuming given the current time ofday, the current day of the week, the patient's current glycemic statusor activity status, and the like based on the patient's historical mealbehavior. The nutritional bolus process 1200 generates or otherwiseprovides an indication of the most probable meal content forconfirmation or acceptance by the patient (tasks 1208, 1210). In one ormore embodiments, a notification of the predicted meal content havingthe highest or greatest probability is generated or otherwise providedon a GUI display for review, modification, and/or confirmation by thepatient. In other embodiments, a listing of a subset of potential mealcontents having the highest or greatest probabilities for the currentcontext are presented on a GUI display for perusal or selection by thepatient. In this regard, the listing of the potential meal contentoptions is personalized and reflects the patient's historical mealbehavior.

For example, for an initial setup monitoring period, the patient mayselect or otherwise indicate the nutritional content, type, orcomponents of a meal when logging meal events while also manuallyinteracting with a bolus wizard or other feature of the infusion device102, 200, 502, 802 or a client application 808 on the client device 106,806 to configure and administer boluses for the contemporaneous mealevents. During the initial setup monitoring period, the patient may alsoperform carbohydrate counting and provide indication of the estimatedcarbohydrate amounts associated with those meals. Once the elapsedduration of the monitoring period is greater than a threshold setupperiod or a sufficient number of meal events have been logged orotherwise documented, the nutritional bolus process 1200 may be enabled.Based on the correlation between one or more of the current time of day,the current day of the week, the current geographic location, thecurrent or recent sensor glucose measurement values, the current orrecent acceleration measurement values, the current or recent heart ratemeasurement values, and potentially other context information withrespect to the context information associated with the patient'shistorical meal events, the nutritional bolus process 1200 determineswhat the most probable meal content is for the current operationalcontext. The nutritional bolus process 1200 then generates or otherwiseprovides a GUI display at the infusion device 102, 200, 502, 802 or theclient device 106, 806 that includes one or more predicted meal typeshaving the highest probability or likelihood given the currentoperational context.

In the illustrated embodiment, in response to selection or confirmationof the predicted meal content, the nutritional bolus process 1200continues by calculating or otherwise determining an estimatedcarbohydrate amount associated with the current meal nutritional content(task 1212). In this regard, based on the patient's historical mealevents having the same type, composition, or nutritional content, anycarbohydrate amounts input or otherwise provided by the patient duringthe initial setup monitoring period may be averaged or otherwiseanalyzed to determine a probable carbohydrate amount associated with thecurrent meal content based on the patient's historical meal sizes forthat type of meal. In some embodiments, the historical meal data for thecurrent meal content may be further limited or analyzed in a contextspecific manner to account for variations in the size of that type ofmeal depending on the current time of day, the current day of the week,or other operational contexts. In yet other embodiments, an estimatedcarbohydrate amount associated with the current meal content may becalculated or determined based on the relationship between the patient'shistorical insulin delivery data and sensor glucose measurement data forhistorical meal events with the common meal content.

The nutritional bolus process 1200 also receives or otherwise obtainsnutritional information associated with the current meal content andcalculates or otherwise determines a meal bolus delivery configurationbased on the nutritional characteristics of the current meal content andthe estimated carbohydrate amount for the current meal (tasks 1214,1216). In this regard, the nutritional bolus process 1200 may adjustbolus dosage amounts or bolus delivery schedules in a manner thataccounts for the postprandial glycemic response to the nutritionalcontent of the meal. Additionally, the bolus delivery configuration mayalso involve modifying closed-loop control information in concert withan adjusted bolus dosage amount to further improve postprandial glucosemanagement given the nutritional content of the meal. After determiningthe meal bolus delivery configuration that accounts for the nutritionalcharacteristics of the meal, the nutritional bolus process 1200 operatesthe infusion device in accordance with the bolus delivery configuration(task 1218).

For example, the remote device 814 and/or the database 816 may storenutritional information associated with different types of meals ornutritional content, such as, for example, a serving size or unit, theamount of carbohydrates per serving size, the amount of fat per servingsize, the amount of protein per serving size, the amount of calories perserving size, the amount of fiber per serving size, the amount of sodiumper serving size, and the like. An application 608, 808 at one of theinfusion device 102, 200, 502, 802 or the client device 106, 806 mayretrieve or otherwise request the nutritional information associatedwith the current meal content from the remote device 814, and thenutilize the nutritional information and the estimated meal size tocalculate or determine a complete nutritional profile for the meal beingconsumed. The application 608, 808 then calculates or otherwisedetermines a meal bolus dosage amount that based on the estimatedcarbohydrate amount that is also adjusted to account for therelationship between the amount of carbohydrates, fat, protein, fiber,and/or other nutritional attributes of the current meal. The application608, 808 may also identify or otherwise determine one or moremodifications to the closed-loop control parameters utilized by theclosed-loop control system 700 supported by the command generationapplication 610. The application 608, 808 then commands, signals, orotherwise instructs the command generation application 610 to deliverthe adjusted meal bolus dosage amounts according to the desired bolusschedule, and depending on the particular embodiment, also temporarilymodify one or more control parameters utilized by the closed-loopcontrol system 700 supported by the command generation application 610for a postprandial period.

For example, for meal content that consists of more fast actingcarbohydrates relative to the amount of fat, fiber, or the like (e.g., asugary or high carbohydrate breakfast), the application 608, 808 mayscale a bolus dosage amount determined by multiplying the estimatedcarbohydrate amount by a carbohydrate ratio by a factor greater than oneto increase the meal bolus amount while also commanding, signaling, orotherwise instructing the command generation application 610 totemporarily suspend delivery by the closed-loop control system 700.Conversely, for meal content that consists of more fat relative to theamount of carbohydrates, the application 608, 808 may scale a bolusdosage amount determined by multiplying the estimated carbohydrateamount by a carbohydrate ratio by a factor less than one to decrease themeal bolus amount while also commanding, signaling, or otherwiseinstructing the command generation application 610 to temporarilyutilize a lower target glucose value 702 and/or increase the minimumand/or maximum basal rate settings to gradually increase insulindelivery during the postprandial period to better account for the mealcontent. It should be noted that the manner or amount of adjustments tothe bolus dosage amount or postprandial closed-loop control adjustmentsmay be personalized or patient-specific and influenced by relationshipsbetween the patient's historical postprandial sensor glucosemeasurements and insulin deliveries associated with historical mealevents having common nutritional content.

Still referring to FIG. 12, when the probable meal content is overruledor otherwise not accepted by the patient, the illustrated nutritionalbolus process 1200 generates or otherwise provides a listing of one ormore alternative meals that are selectable by the patient (task 1220).In this regard, a personalized, patient-specific library of potentialmeal content may be created based on the patient's historical meal dataand utilized to present additional meal options to the patient. Forexample, when the most probable meal content originally displayed by thenutritional bolus process 1200 is not confirmed by the patient, thenutritional bolus process 1200 may provide a listing or library of mealcontent corresponding to one or more of the patient's historical mealevents sorted in descending order according to their respectiveprobabilities or likelihoods given the current operational context(e.g., time of day, day of week, current sensor glucose measurementvalue, current geographic location, and/or the like). Thus, the numberof user inputs required by the user to select the appropriate mealcontent may be reduced by presenting meal content options that are knownto have been consumed by the patient. The nutritional bolus process 1200receives user input indicative of the current meal content (task 1222)and continues by determining a meal bolus delivery configuration for theinput meal content in a similar manner as described above. For example,the patient may scroll the personalized list of meal content and selectthe current meal content from the list, and in response, historical mealdata corresponding to the selected meal content is analyzed to identifythe estimated meal size or number of servings being consumed in asimilar manner as described above.

In one or more embodiments, the nutritional bolus process 1200 may beconfigured to allow the patient to input or otherwise indicate the mealcontent by uploading a photograph or image of the meal being consumed.In this regard, either the client application 808 at the client device806 or the remote device 814 may support object recognition or otherimage processing artificial intelligence that allows a submitted oruploaded photograph to be mapped to a particular type of meal content.In one or more embodiments, the client application 808 at the clientdevice 806 or the remote device 814 may support a neural network modelor similar artificial intelligence model that is trained using imagescorresponding to the patient's historical meal data, so that therecognition accuracy is improved for uploaded images of meal contentpreviously consumed by the patient.

Similar to the personalized bolus process 1100 of FIG. 11, thenutritional bolus process 1200 allows the patient to merely confirm themost probable meal content or provide indication of an alternativewithout having to resort to carbohydrate counting, meal size estimation,and/or the like. It should be noted that the nutritional bolus process1200 may be performed in concert with the prospective closed-loopcontrol process 900 and the event pattern control process 1000 toimprove the patient's glucose management. In this regard, as describedabove, the prospective closed-loop control process 900 automaticallyincreases the insulin delivery rate or insulin on board in advance ofthe meal. The nutritional bolus process 1200 may then be initiatedautomatically at the predicted meal time or in response to the eventpattern control process 1000 detecting the occurrence of a meal. Thenutritional bolus process 1200 then determines a personalized,context-specific meal bolus delivery configuration corresponding to thenutritional content of the meal that was automatically identified,detected, or predicted. The patient may then simply confirm thepredicted meal content and trigger administration of a meal bolus withas little as a single user input without any carbohydrate counting orother manual interaction. Additionally, in embodiments where closed-loopadjustments are also performed according to the nutritional content ofthe meal, variations in the rate of glucose appearance depending on themeal content can be accounted for or otherwise mitigated to improveefficacy of postprandial glucose management. It should be noted that thenutritional bolus process 1200 may also be performed in concert with thepersonalized bolus process 1100, for example, to further adjust orrefine the personalized meal bolus amount for the current meal size(e.g., from task 1112) or adjust closed-loop control information for apostprandial period to account for the nutritional content or componentsof the meal, and thereby improve postprandial glucose management.

Personalized Adjustments for Patient Activity

FIG. 13 depicts an exemplary patient activity control process 1300suitable for implementation by an infusion device (or a control systemassociated therewith) to adjust a bolus amount or closed-loop controlinformation for a postprandial period in a personalized manner thataccounts for activity that the patient is or will be engaged in. In thisregard, patient activity control process 1300 allows for the patient toindicate what activity or activities the patient is or will be engagedin that could influence his or her insulin response, which, in turn maybe utilized to increase or decrease insulin delivery in a personalizedmanner based on the historical effects of that activity on the patient'sglycemic status.

The various tasks performed in connection with the patient activitycontrol process 1300 may be performed by hardware, firmware, softwareexecuted by processing circuitry, or any combination thereof. Forillustrative purposes, the following description refers to elementsmentioned above in connection with FIGS. 1-8. In practice, portions ofthe patient activity control process 1300 may be performed by differentelements of an infusion system, such as, for example, an infusion device102, 200, 502, 802, a client computing device 106, 806, a remotecomputing device 108, 814, and/or a pump control system 520, 600. Itshould be appreciated that the patient activity control process 1300 mayinclude any number of additional or alternative tasks, the tasks neednot be performed in the illustrated order and/or the tasks may beperformed concurrently, and/or the patient activity control process 1300may be incorporated into a more comprehensive procedure or processhaving additional functionality not described in detail herein.Moreover, one or more of the tasks shown and described in the context ofFIG. 13 could be omitted from a practical embodiment of the patientactivity control process 1300 as long as the intended overallfunctionality remains intact.

The patient activity control process 1300 begins by generating orotherwise prompting the patient to define the current operationalcontext for a bolus in terms of the activity that the patient is or willbe engaged in and receiving or otherwise obtaining indication of one ormore activities that the patient is or will be engaged in from thepatient (tasks 1302, 1304). In one or more exemplary embodiments, thepatient activity control process 1300 is initiated during patientinteraction with a bolus wizard feature or other GUI display that isutilized to configure or administer meal boluses. For example, theclient application 808 at the client device 806 may generate orotherwise provide a GUI display that includes a list of selectable GUIelements corresponding to different activities that the patient couldengage in after the patient has input or confirmed informationcharacterizing the current meal to be bolused. For example, the patientactivity control process 1300 may be performed in concert with thepersonalized bolus process 1100 after confirming the size of the meal tobe bolused and/or in concert with the nutritional bolus process 1200after confirming the nutritional content of the meal to be bolused. TheGUI display then presented by the client application 808 at the clientdevice 806 may include selectable GUI elements corresponding toexercise, sleep, work, stress, travel, or other types of activities thatthe patient might engage in in the immediate future. Moreover, the GUIdisplay may be configured to allow the patient to select or defineattributes or characteristics associated with those activities, such as,for example, the intensity or type of exercise (e.g., aerobic,anaerobic, cardio, strength training, and/or the like), the duration oftime the patient intends to sleep (e.g., whether the sleep correspondsto an overnight period or a nap), and/or the like. The patient may thenmanipulate the GUI elements on the GUI display to indicate and definethe activity that the patient is currently engaged in contemporaneous tothe meal bolus or will be engaged in thereafter.

Based on the input activity, the patient activity control process 1300retrieves or otherwise obtains historical patient data corresponding tohistorical events matching that activity and then adjust or modifiescurrent or future insulin deliveries based on the patient's historicalglycemic response to the activity (tasks 1306, 1308). In this regard, anapplication 608, 808 at one of the infusion device 102, 200, 502, 802 orthe client device 106, 806 may retrieve or otherwise request historicalsensor glucose measurement data and historical insulin delivery datacontemporaneous or concurrent to past occurrences of the input activity.For example, the client application 808 may support the patient loggingor journaling various activities or lifestyle events and uploading theevent log data to the remote device 814 for storage in the database 816.The event log data may include a time or timestamp associated with theparticular activity or event, the day of the week associated with theactivity or event, its associated geolocation, the duration of theactivity, and potentially other attributes of the activity. Theapplication 608, 808 at one of the infusion device 102, 200, 502, 802 orthe client device 106, 806 may request event log data associated withactivities or events that match the input activity, and then utilize thetemporal information associated with those historical events (e.g.,timestamps and durations) to select corresponding subsets of thepatient's historical measurement data and insulin delivery datacontemporaneous or concurrent to those historical events. Therelationship between the patient's historical measurement data andinsulin delivery data corresponding to the historical occurrences of theinput activity may be modeled or otherwise analyzed to determine thepatient's average or likely glycemic response to the input activity, forexample, by comparing the relationships between the subsets of thepatient's historical measurement data and the insulin delivery datacorresponding to the historical occurrences to the relationships betweenthe patient's historical measurement data and insulin delivery datagenerally. For example, the patient's historical glycemic response whenthe patient engaged in a particular type of exercise within a thresholdperiod of time after a meal may be compared to when the patient'shistorical glycemic response when the patient did not engage in exercisewithin that time period after a meal.

Once the patient's historical glycemic response to the input activity isquantified or otherwise characterized, the application 608, 808supporting the patient activity control process 1300 automaticallyadjusts the bolus delivery configuration in a manner that corresponds tothe patient's historical glycemic response to the input activity. Forexample, if the relationship between the patient's historicalmeasurement data and insulin delivery data corresponding to thehistorical occurrences of the input activity indicate that the patient'sglycemic response requires roughly 20% less insulin delivered than whenthe patient does not engage in the activity, the application 608, 808may automatically reduce the calculated meal bolus amount by 20% oradjust one or more closed-loop control parameters for the postprandialperiod to reduce the insulin delivery by roughly 20%. In this manner,the likelihood of postprandial hypoglycemia or a need to subsequentlyconsume additional carbohydrates may be reduced. Similarly, if thepatient's historical glycemic response indicates more insulin isrequired to avoid postprandial hyperglycemia, the bolus amount orclosed-loop control information may be adjusted to increase insulindelivery to avoid postprandial hyperglycemia or subsequent correctionboluses.

In one or more exemplary embodiments, machine learning or other modelingis utilized to predict one or more characteristics of the patient'sfuture activity, which, in turn, may be utilized to determine theprobable glycemic response and corresponding amount or duration ofinsulin delivery adjustments to be made in a manner that is influencedby the predicted characteristics of the future activity. For example, anexpected duration, an expected intensity, and/or other attributes of thefuture activity may be predicted by determining which combinations orsubsets of historical measurement data, historical event log data, andhistorical operational contexts (e.g., time of day, day of week,geographic location, and the like) are correlative or predictive of theintensity, duration, or other characteristic or attribute of aparticular activity. A corresponding equation, function, or model forcalculating a metric or value representative of the duration, intensity,or other characteristic for the future activity may be determined thatmay then be applied to the current operational context or the futureoperational context associated with the future activity to determine theexpected duration, intensity, or other characteristic given the currentoperational context or the future operational context.

For example, the duration or intensity associated with historicalexercise events may be utilized to construct a model that allows theapplication 608, 808 supporting the patient activity control process1300 to predict an expected duration or intensity for an anticipatedfuture exercise event as a function of the time of day, the day of theweek, the geographic location, current or recent sensor glucosemeasurements, current or recently logged events, and/or the like.Similarly, a model may also be constructed that models the patient'sglycemic response to a particular duration or intensity of exercise as afunction of the current time of day (or a future time of day associatedwith the anticipated exercise event), the current day of the week, thecurrent geographic location, current or recent sensor glucosemeasurements, current or recently logged events, and/or the like basedon correlations between historical operational contexts and historicalsensor glucose measurements associated with historical exercise eventshaving similar durations or intensities. The model appropriate for thepredicted duration or intensity for the future exercise event may thusbe utilized to determine the probable glycemic response, which, in turn,is utilized to adjust bolus dosages amounts or schedules or closed-loopcontrol parameters in an appropriate manner. A model may also bedeveloped that allows for a predicted future time of day in advance ofthe current time to be calculated based at least in part on thehistorical times of day associated with previous exercise events, withthe predicted future time of day then being input to another model forpredicting exercise characteristics or glycemic response based on whenthe future exercise event is most likely to occur.

Similarly, when the future activity by the patient is sleeping, theduration associated with previous sleep events may be utilized toconstruct a model that allows the application 608, 808 to predict anexpected duration of sleep as a function of the time of day (e.g.,napping versus overnight), the day of the week, the geographic location,and potentially other contextual factors. Similarly, the patient'sglycemic response during sleep may be modeled in a manner that accountsfor the duration of sleep, the current time of day, the current day ofthe week, the current geographic location, current or recent sensorglucose measurements, current or recently logged events, and/or thelike. It should be noted that in some embodiments, the expected durationfor a future sleep event may be utilized to influence the duration ofinsulin delivery adjustments that are implemented by the patientactivity control process 1300. For example, a target glucose value orother closed-loop control parameter may be maintained at an adjustedvalue for a duration of time configured to overlap with or otherwisecorrespond to the duration of time during which the user is expected tobe sleeping before reverting to the original, preceding, or unadjustedvalue. In a similar manner, adjustments for an exercise event may beconfigured to temporarily align with the expected duration of theexercise before reverting to their preceding state or value.

It should be noted that the patient activity control process 1300 may beperformed in concert with any or all of the processes described above inthe context of FIGS. 9-12 to adjust bolus amounts or closed-loop controlinformation in anticipation of subsequent patient activity andcooperatively improve glucose management. For example, the prospectiveclosed-loop control process 900 may proactively load insulin in advanceof a meal, which subsequently be detected by the event pattern controlprocess 1000, which, in turn, triggers the personalized bolus process1100 and/or the nutritional bolus process 1200 to determine meal bolusdosage amounts and any other closed-loop control adjustments for apostprandial period. The patient activity control process 1300 may thenbe performed to further modify the meal bolus dosage amount orclosed-loop control parameters determined by the personalized bolusprocess 1100 and/or the nutritional bolus process 1200 to account forthe anticipated patient activity, thereby improving postprandial glucosemanagement. It should be noted that the event pattern control process1000 may subsequently detect occurrence of the anticipated patientactivity, and further fine tune or adjust closed-loop control parametersat that time. In such scenarios, the patient activity control process1300 effectively performs prospective adjustments to the patient'sinsulin delivery before any real-time adjustments that may be performedin connection with the event pattern control process 1000 to furtherimprove glucose management throughout the duration of the activity.

While the patient activity control process 1300 may be primarilydescribed in the context of modifications that account for future eventsor activities, the patient activity control process 1300 may beimplemented in an equivalent manner for contemporaneous, concurrent, orpreceding events or activities. Thus, the patient activity controlprocess 1300 may be able to proactively account for past or currentactivities or events that have not yet exhibited a correspondingglycemic response in advance of any potential glucose excursions.

Referring to FIG. 8 with reference to FIGS. 9-13, it should be notedthat in one or more embodiments, various aspects of the processes ofFIGS. 9-13 may be distributed across different devices in a patientmonitoring system 800. For example, sensor glucose measurement data andother measurement data pertaining to the patient may be periodicallyuploaded from a medical device 802, such as a sensing arrangement 104,504, 506 or an infusion device 102, 200, 502, to the remote device 814(either directly or indirectly by way of an intermediary device 806) forstorage in the database 816 in association with the patient. Similarly,event log data including meal indicia, exercise indicia, and otherinformation characterizing events and lifestyle activities may beuploaded from the client application 808 at the client device 806 to theremote device 814 for storage in the database 816 in association withthe patient.

The remote device 814 may then periodically analyze the relationshipsbetween the patient's measurement and event log data to generatepersonalized patient-specific models, for example, on a daily basis, aweekly basis, a biweekly basis, a monthly basis, or the like. The remotedevice 814 may generate various patient-specific models for differentoperational contexts (e.g., for different times or periods of the day,for different days of the week, for different geographic locations orregions, and the like) and dynamically update the models on a periodicbasis using machine learning or other artificial intelligence techniquesto account for recent measurement and event log data added to thedatabase 816 since the models were previously generated and adaptivelytrack changes in the patient's behavior.

Depending on the embodiment, the remote device 814 may store or maintainthe models in the database 816 in association with the patient forsubsequent retrieval by a device 802, 806 associated with the patient,or alternatively, the remote device 814 may automatically push updatedmodels to one or more devices 802, 806 associated with the patient. Theinfusion device 802 or the client device 806 associated with the patientmay then select or otherwise identify the appropriate patient-specificmodels provided by the remote device 814 for the current operationalcontext and then utilize those models to support the processes of FIGS.9-13 described above. For example, in response to the patientmanipulating the client application 808 to add a meal, exercise, orother activity to his or her event log, the client application 808 mayselect the appropriate models or functions for generating theappropriate bolus dosage amounts or control adjustments given thecurrent operational context and responsive to user inputs received viathe client application 808 (e.g., indicia of meal size, meal content,postprandial activities, and/or the like). The client application 808may then transmit or otherwise provide corresponding commands, signals,or instructions to the infusion device 802, which, in turn, altersinsulin delivery in a patient-specific and context-sensitive manner asdescribed above. In yet other embodiments, the remote device 814 mayreceive current or recent measurement and/or event log data from one ormore of the devices 802, 806, apply the appropriate patient-specificmodels to the current or recent data at the remote device 814 todetermine commands for altering or adjusting operation of the infusiondevice 802, and then transmitting or otherwise providing such commandsto the infusion device 802 (e.g., via the client device 806). Thus,depending on the embodiment, the control system associated with theinfusion device 802 that is supporting or otherwise implementing arespective process of FIGS. 9-13 could reside at any one of the devices802, 806, 814.

It should be noted that in one or more embodiments, the patient modelmay be normalized or augmented using broader population data. Forexample, a population model may be developed by the remote device 814 byanalyzing relationships between measurement data, event log data, andoperational context across a plurality of different patients. In someembodiments, patients may be assigned or otherwise associated with aparticular group of patients having one or more characteristics incommon based on the demographic information associated with thatpatient, with a probabilistic predictive model for that patient groupbeing determined based on the aggregated historical data for thedifferent patients of the group. In this regard, in the absence ofsufficient historical data in the database 816 for a particular patient,a model associated with that patient's population group may be utilizedto predict the patient's behavior and/or the patient's likely glycemicresponse. As the amount of historical data associated with the patientincreases, the remote device 814 may transition to determining andpushing patient-specific models to the patient's associated device(s)802, 806. In this regard, in some embodiments, a result, outcome, oroutput produced using a patient-specific model may be progressivelyweighted or otherwise combined with a corresponding result, outcome, oroutput produced using a population group model in accordance with theamount of available historical data associated with that patient in amanner that reflects the level of accuracy, reliability, or confidencein the patient-specific model until a threshold amount of historicaldata is obtained that allows sole reliance on the patient-specificmodel.

FIGS. 14-15 depict one exemplary sequence of GUI displays that may bepresented on an electronic device in accordance with one or more of theprocesses of FIGS. 9-13 described above. For example, FIG. 14 depicts ameal size GUI display 1400 that may be presented by the clientapplication 808 on the computing device 806 to enable the patient toinput the size of a meal to be bolused for. In some embodiments, themeal size GUI display 1400 is automatically presented at a predictedmeal time or when the probability of a meal being consumed at thecurrent time is greater than a threshold probability (e.g., greater than75%). In other embodiments, the meal size GUI display 1400 isautomatically presented in response to detecting a meal event pattern.In yet other embodiments, the meal size GUI display 1400 may bepresented as part of a bolus wizard feature of the client application808 in lieu of presenting a GUI display for inputting carbohydratecounts. The meal size GUI display 1400 includes a list 1402 of GUIelements corresponding to different qualitative meal sizes, which areselectable by a user to input or otherwise indicate the size of the mealto be bolused. As described above, the patient's historical meal dataand potentially other factors or data (e.g., time of day, day of week,geographic location, historical glycemic response, historical bolusdosages, and the like) may be utilized to convert the selected meal sizeindicated by the patient into a probable carbohydrate amount to bebolused for.

FIG. 15 depicts an activity GUI display 1500 that may be presented bythe client application 808 on the computing device 806 to enable thepatient to select or otherwise indicate activities he or she is likelyto engage in within a postprandial period after administrating a bolusin connection with the patient activity control process 1300 of FIG. 13after inputting a meal size using the meal size GUI display 1400. Theactivity GUI display 1500 includes a list 1502 of GUI elementscorresponding to different activities that the patient may subsequentlyengage in that are likely to influence the patient's glycemic responseor insulin sensitivity. As described above, the patient's historicalmeal, measurement, and delivery data associated with the selectedactivity and potentially other contextual factors may be analyzed todetermine a probable glycemic response for the patient given the currentoperational context, which, in turn, may be utilized to adjust acalculated meal bolus amount or closed-loop control parameters toaccount for the patient's prospective postprandial activity. It shouldbe noted that some embodiments of the patient activity control process1300 and the activity GUI display 1500 may also be configured to supportaccounting for preprandial activities or other activities concurrent toor preceding a meal in an equivalent manner.

FIG. 16 depicts an exemplary GUI display 1600 that may be presented on adisplay device (e.g., display 226) associated with an infusion device1602, such as any one of the infusion devices 102, 200, 502, 802described above, in accordance with the event pattern control process1000 of FIG. 10. For example, in response to detecting a pattern in thepatient sensor glucose measurements that indicates that the patient haslikely consumed breakfast based on the current time of day, the currentday of the week, and/or potentially other factors with a relatively highenough probability, the personalization application 608 may generate orotherwise provide the GUI display 1600 to provide a graphical usernotification that a breakfast pattern has been detected. The GUI display1600 includes a notification region that includes textual informationcharacterizing or describing the detected event pattern, along with GUIelements 1604 that are selectable by the patient to confirm orinvalidate the detected activity. When patient selects a GUI element1604 to confirm or validate the detected activity, the personalizationapplication 608 may automatically adjust insulin delivery according tothe patient's historical data and historical response to the detectedactivity given the current operational context as described above (e.g.,task 1014).

FIG. 17 depicts an exemplary GUI display 1700 that may be presented on adisplay device associated with a client device 1706, such as any one ofthe client computing devices 106, 806 described above, in accordancewith the event pattern control process 1000. Similar to the aboveexample, in response to the client application 808 detecting a patternin the patient's current or recent measurement data or other operationalcontext information that indicates that the patient has likely consumedbreakfast with a sufficiently great enough probability, the clientapplication 808 may generate or otherwise provide the event patternnotification GUI display 1700 to graphically notify the patient that abreakfast pattern has been detected. The illustrated event patternnotification GUI display 1700 includes a list 1702 with a plurality ofGUI elements that are selectable by the patient to confirm or invalidatethe detected activity. The list 1702 also includes selectable GUIelements that allow the patient to confirm the detected activity butmodify one or more attributes or characteristics of the activityrelative to the patient's typical or usual activity given the currentoperational context. For example, in the illustrated embodiments, theGUI display 1700 includes selectable GUI elements that allow the patientto indicate whether a detected meal event pattern corresponds to a mealthat deviates in size (or alternatively nutritional content or type)relative to the patient's typical meals at that time of day, day ofweek, etc. When patient selects a GUI element to confirm the detectedactivity but modify a characteristic or attribute thereof, the clientapplication 808 may automatically adjust insulin delivery according tothe patient's historical data and historical response to the detectedactivity given the current operational context in a manner that alsoaccounts for the input modification by the patient, for example, byfurther adjusting the insulin delivery up or down based on whether thepatient indicates the current meal is larger or smaller than normal forthe current operational context.

Diabetes Data Management System Overview

FIG. 18 illustrates a computing device 1800 suitable for use as part ofa diabetes data management system in conjunction with one or more of theprocesses described above in the context of FIGS. 9-13. The diabetesdata management system (DDMS) may be referred to as the MedtronicMiniMed CARELINK™ system or as a medical data management system (MDMS)in some embodiments. The DDMS may be housed on a server or a pluralityof servers which a user or a health care professional may access via acommunications network via the Internet or the World Wide Web. Somemodels of the DDMS, which is described as an MDMS, are described in U.S.Patent Application Publication Nos. 2006/0031094 and 2013/0338630, whichis herein incorporated by reference in their entirety.

While description of embodiments are made in regard to monitoringmedical or biological conditions for subjects having diabetes, thesystems and processes herein are applicable to monitoring medical orbiological conditions for cardiac subjects, cancer subjects, HIVsubjects, subjects with other disease, infection, or controllableconditions, or various combinations thereof.

In embodiments of the invention, the DDMS may be installed in acomputing device in a health care provider's office, such as a doctor'soffice, a nurse's office, a clinic, an emergency room, an urgent careoffice. Health care providers may be reluctant to utilize a system wheretheir confidential patient data is to be stored in a computing devicesuch as a server on the Internet.

The DDMS may be installed on a computing device 1800. The computingdevice 1800 may be coupled to a display 1833. In some embodiments, thecomputing device 1800 may be in a physical device separate from thedisplay (such as in a personal computer, a mini-computer, etc.) In someembodiments, the computing device 1800 may be in a single physicalenclosure or device with the display 1833 such as a laptop where thedisplay 1833 is integrated into the computing device. In embodiments ofthe invention, the computing device 1800 hosting the DDMS may be, but isnot limited to, a desktop computer, a laptop computer, a server, anetwork computer, a personal digital assistant (PDA), a portabletelephone including computer functions, a pager with a large visibledisplay, an insulin pump including a display, a glucose sensor includinga display, a glucose meter including a display, and/or a combinationinsulin pump/glucose sensor having a display. The computing device mayalso be an insulin pump coupled to a display, a glucose meter coupled toa display, or a glucose sensor coupled to a display. The computingdevice 1800 may also be a server located on the Internet that isaccessible via a browser installed on a laptop computer, desktopcomputer, a network computer, or a PDA. The computing device 1800 mayalso be a server located in a doctor's office that is accessible via abrowser installed on a portable computing device, e.g., laptop, PDA,network computer, portable phone, which has wireless capabilities andcan communicate via one of the wireless communication protocols such asBluetooth and IEEE 802.11 protocols.

In the embodiment shown in FIG. 18, the data management system 1816comprises a group of interrelated software modules or layers thatspecialize in different tasks. The system software includes a devicecommunication layer 1824, a data parsing layer 1826, a database layer1828, database storage devices 1829, a reporting layer 1830, a graphdisplay layer 1831, and a user interface layer 1832. The diabetes datamanagement system may communicate with a plurality of subject supportdevices 1812, two of which are illustrated in FIG. 18. Although thedifferent reference numerals refer to a number of layers, (e.g., adevice communication layer, a data parsing layer, a database layer),each layer may include a single software module or a plurality ofsoftware modules. For example, the device communications layer 1824 mayinclude a number of interacting software modules, libraries, etc. Inembodiments of the invention, the data management system 1816 may beinstalled onto a non-volatile storage area (memory such as flash memory,hard disk, removable hard, DVD-RW, CD-RW) of the computing device 1800.If the data management system 1816 is selected or initiated, the system1816 may be loaded into a volatile storage (memory such as DRAM, SRAM,RAM, DDRAM) for execution.

The device communication layer 1824 is responsible for interfacing withat least one, and, in further embodiments, to a plurality of differenttypes of subject support devices 1812, such as, for example, bloodglucose meters, glucose sensors/monitors, or an infusion pump. In oneembodiment, the device communication layer 1824 may be configured tocommunicate with a single type of subject support device 1812. However,in more comprehensive embodiments, the device communication layer 1824is configured to communicate with multiple different types of subjectsupport devices 1812, such as devices made from multiple differentmanufacturers, multiple different models from a particular manufacturerand/or multiple different devices that provide different functions (suchas infusion functions, sensing functions, metering functions,communication functions, user interface functions, or combinationsthereof). By providing an ability to interface with multiple differenttypes of subject support devices 1812, the diabetes data managementsystem 1816 may collect data from a significantly greater number ofdiscrete sources. Such embodiments may provide expanded and improveddata analysis capabilities by including a greater number of subjects andgroups of subjects in statistical or other forms of analysis that canbenefit from larger amounts of sample data and/or greater diversity insample data, and, thereby, improve capabilities of determiningappropriate treatment parameters, diagnostics, or the like.

The device communication layer 1824 allows the DDMS 1816 to receiveinformation from and transmit information to or from each subjectsupport device 1812 in the system 1816. Depending upon the embodimentand context of use, the type of information that may be communicatedbetween the system 1816 and device 1812 may include, but is not limitedto, data, programs, updated software, education materials, warningmessages, notifications, device settings, therapy parameters, or thelike. The device communication layer 1824 may include suitable routinesfor detecting the type of subject support device 1812 in communicationwith the system 1816 and implementing appropriate communicationprotocols for that type of device 1812. Alternatively or in addition,the subject support device 1812 may communicate information in packetsor other data arrangements, where the communication includes a preambleor other portion that includes device identification information foridentifying the type of the subject support device. Alternatively, or inaddition, the subject support device 1812 may include suitableuser-operable interfaces for allowing a user to enter information (e.g.,by selecting an optional icon or text or other device identifier) thatcorresponds to the type of subject support device used by that user.Such information may be communicated to the system 1816, through anetwork connection. In yet further embodiments, the system 1816 maydetect the type of subject support device 1812 it is communicating within the manner described above and then may send a message requiring theuser to verify that the system 1816 properly detected the type ofsubject support device being used by the user. For systems 1816 that arecapable of communicating with multiple different types of subjectsupport devices 1812, the device communication layer 1824 may be capableof implementing multiple different communication protocols and selects aprotocol that is appropriate for the detected type of subject supportdevice.

The data-parsing layer 1826 is responsible for validating the integrityof device data received and for inputting it correctly into a database1829. A cyclic redundancy check CRC process for checking the integrityof the received data may be employed. Alternatively, or in addition,data may be received in packets or other data arrangements, wherepreambles or other portions of the data include device typeidentification information. Such preambles or other portions of thereceived data may further include device serial numbers or otheridentification information that may be used for validating theauthenticity of the received information. In such embodiments, thesystem 1816 may compare received identification information withpre-stored information to evaluate whether the received information isfrom a valid source.

The database layer 1828 may include a centralized database repositorythat is responsible for warehousing and archiving stored data in anorganized format for later access, and retrieval. The database layer1828 operates with one or more data storage device(s) 1829 suitable forstoring and providing access to data in the manner described herein.Such data storage device(s) 1829 may comprise, for example, one or morehard discs, optical discs, tapes, digital libraries or other suitabledigital or analog storage media and associated drive devices, drivearrays or the like.

Data may be stored and archived for various purposes, depending upon theembodiment and environment of use. Information regarding specificsubjects and patient support devices may be stored and archived and madeavailable to those specific subjects, their authorized healthcareproviders and/or authorized healthcare payor entities for analyzing thesubject's condition. Also, certain information regarding groups ofsubjects or groups of subject support devices may be made available moregenerally for healthcare providers, subjects, personnel of the entityadministering the system 1816 or other entities, for analyzing groupdata or other forms of conglomerate data.

Embodiments of the database layer 1828 and other components of thesystem 1816 may employ suitable data security measures for securingpersonal medical information of subjects, while also allowingnon-personal medical information to be more generally available foranalysis. Embodiments may be configured for compliance with suitablegovernment regulations, industry standards, policies or the like,including, but not limited to the Health Insurance Portability andAccountability Act of 1996 (HIPAA).

The database layer 1828 may be configured to limit access of each userto types of information pre-authorized for that user. For example, asubject may be allowed access to his or her individual medicalinformation (with individual identifiers) stored by the database layer1828, but not allowed access to other subject's individual medicalinformation (with individual identifiers). Similarly, a subject'sauthorized healthcare provider or payor entity may be provided access tosome or all of the subject's individual medical information (withindividual identifiers) stored by the database layer 1828, but notallowed access to another individual's personal information. Also, anoperator or administrator-user (on a separate computer communicatingwith the computing device 1800) may be provided access to some or allsubject information, depending upon the role of the operator oradministrator. On the other hand, a subject, healthcare provider,operator, administrator or other entity, may be authorized to accessgeneral information of unidentified individuals, groups or conglomerates(without individual identifiers) stored by the database layer 1828 inthe data storage devices 1829.

In exemplary embodiments, the database 1829 stores uploaded measurementdata for a patient (e.g., sensor glucose measurement and characteristicimpedance values) along with event log data consisting of event recordscreated during a monitoring period corresponding to the measurementdata. In embodiments of the invention, the database layer 1828 may alsostore preference profiles. In the database layer 1828, for example, eachuser may store information regarding specific parameters that correspondto the user. Illustratively, these parameters could include target bloodglucose or sensor glucose levels, what type of equipment the usersutilize (insulin pump, glucose sensor, blood glucose meter, etc.) andcould be stored in a record, a file, or a memory location in the datastorage device(s) 1829 in the database layer. Preference profiles mayinclude various threshold values, monitoring period values,prioritization criteria, filtering criteria, and/or other user-specificvalues for parameters to generate a snapshot GUI display on the display1833 or a support device 1812 in a personalized or patient-specificmanner.

The DDMS 1816 may measure, analyze, and track either blood glucose (BG)or sensor glucose (SG) measurements (or readings) for a user. Inembodiments of the invention, the medical data management system maymeasure, track, or analyze both BG and SG readings for the user.Accordingly, although certain reports may mention or illustrate BG or SGonly, the reports may monitor and display results for the other one ofthe glucose readings or for both of the glucose readings.

The reporting layer 1830 may include a report wizard program that pullsdata from selected locations in the database 1829 and generates reportinformation from the desired parameters of interest. The reporting layer1830 may be configured to generate multiple different types of reports,each having different information and/or showing information indifferent formats (arrangements or styles), where the type of report maybe selectable by the user. A plurality of pre-set types of report (withpre-defined types of content and format) may be available and selectableby a user. At least some of the pre-set types of reports may be common,industry standard report types with which many healthcare providersshould be familiar. In exemplary embodiments described herein, thereporting layer 1830 also facilitates generation of a snapshot reportincluding a snapshot GUI display.

In embodiments of the invention, the database layer 1828 may calculatevalues for various medical information that is to be displayed on thereports generated by the report or reporting layer 1830. For example,the database layer 1828, may calculate average blood glucose or sensorglucose readings for specified timeframes. In embodiments of theinvention, the reporting layer 1830 may calculate values for medical orphysical information that is to be displayed on the reports. Forexample, a user may select parameters which are then utilized by thereporting layer 1830 to generate medical information valuescorresponding to the selected parameters. In other embodiments of theinvention, the user may select a parameter profile that previouslyexisted in the database layer 1828.

Alternatively, or in addition, the report wizard may allow a user todesign a custom type of report. For example, the report wizard may allowa user to define and input parameters (such as parameters specifying thetype of content data, the time period of such data, the format of thereport, or the like) and may select data from the database and arrangethe data in a printable or displayable arrangement, based on theuser-defined parameters. In further embodiments, the report wizard mayinterface with or provide data for use by other programs that may beavailable to users, such as common report generating, formatting orstatistical analysis programs. In this manner, users may import datafrom the system 1816 into further reporting tools familiar to the user.The reporting layer 1830 may generate reports in displayable form toallow a user to view reports on a standard display device, printableform to allow a user to print reports on standard printers, or othersuitable forms for access by a user. Embodiments may operate withconventional file format schemes for simplifying storing, printing andtransmitting functions, including, but not limited to PDF, JPEG, or thelike. Illustratively, a user may select a type of report and parametersfor the report and the reporting layer 1830 may create the report in aPDF format. A PDF plug-in may be initiated to help create the report andalso to allow the user to view the report. Under these operatingconditions, the user may print the report utilizing the PDF plug-in. Incertain embodiments in which security measures are implemented, forexample, to meet government regulations, industry standards or policiesthat restrict communication of subject's personal information, some orall reports may be generated in a form (or with suitable softwarecontrols) to inhibit printing, or electronic transfer (such as anon-printable and/or non-capable format). In yet further embodiments,the system 1816 may allow a user generating a report to designate thereport as non-printable and/or non-transferable, whereby the system 1816will provide the report in a form that inhibits printing and/orelectronic transfer.

The reporting layer 1830 may transfer selected reports to the graphdisplay layer 1831. The graph display layer 1831 receives informationregarding the selected reports and converts the data into a format thatcan be displayed or shown on a display 1833.

In embodiments of the invention, the reporting layer 1830 may store anumber of the user's parameters. Illustratively, the reporting layer1830 may store the type of carbohydrate units, a blood glucose movementor sensor glucose reading, a carbohydrate conversion factor, andtimeframes for specific types of reports. These examples are meant to beillustrative and not limiting.

Data analysis and presentations of the reported information may beemployed to develop and support diagnostic and therapeutic parameters.Where information on the report relates to an individual subject, thediagnostic and therapeutic parameters may be used to assess the healthstatus and relative well-being of that subject, assess the subject'scompliance to a therapy, as well as to develop or modify treatment forthe subject and assess the subject's behaviors that affect his/hertherapy. Where information on the report relates to groups of subjectsor conglomerates of data, the diagnostic and therapeutic parameters maybe used to assess the health status and relative well-being of groups ofsubjects with similar medical conditions, such as, but not limited to,diabetic subjects, cardiac subjects, diabetic subjects having aparticular type of diabetes or cardiac condition, subjects of aparticular age, sex or other demographic group, subjects with conditionsthat influence therapeutic decisions such as but not limited topregnancy, obesity, hypoglycemic unawareness, learning disorders,limited ability to care for self, various levels of insulin resistance,combinations thereof, or the like.

The user interface layer 1832 supports interactions with the end user,for example, for user login and data access, software navigation, datainput, user selection of desired report types and the display ofselected information. Users may also input parameters to be utilized inthe selected reports via the user interface layer 1832. Examples ofusers include but are not limited to: healthcare providers, healthcarepayer entities, system operators or administrators, researchers,business entities, healthcare institutions and organizations, or thelike, depending upon the service being provided by the system anddepending upon the invention embodiment. More comprehensive embodimentsare capable of interacting with some or all of the above-noted types ofusers, wherein different types of users have access to differentservices or data or different levels of services or data.

In an example embodiment, the user interface layer 1832 provides one ormore websites accessible by users on the Internet. The user interfacelayer may include or operate with at least one (or multiple) suitablenetwork server(s) to provide the website(s) over the Internet and toallow access, world-wide, from Internet-connected computers usingstandard Internet browser software. The website(s) may be accessed byvarious types of users, including but not limited to subjects,healthcare providers, researchers, business entities, healthcareinstitutions and organizations, payor entities, pharmaceutical partnersor other sources of pharmaceuticals or medical equipment, and/or supportpersonnel or other personnel running the system 1816, depending upon theembodiment of use.

In another example embodiment, where the DDMS 1816 is located on onecomputing device 1800, the user interface layer 1832 provides a numberof menus to the user to navigate through the DDMS. These menus may becreated utilizing any menu format, including but not limited to HTML,XML, or Active Server pages. A user may access the DDMS 1816 to performone or more of a variety of tasks, such as accessing general informationmade available on a website to all subjects or groups of subjects. Theuser interface layer 1832 of the DDMS 1816 may allow a user to accessspecific information or to generate reports regarding that subject'smedical condition or that subject's medical device(s) 1812, to transferdata or other information from that subject's support device(s) 1812 tothe system 1816, to transfer data, programs, program updates or otherinformation from the system 1816 to the subject's support device(s)1812, to manually enter information into the system 1816, to engage in aremote consultation exchange with a healthcare provider, or to modifythe custom settings in a subject's supported device and/or in asubject's DDMS/MDMS data file.

The system 1816 may provide access to different optional resources oractivities (including accessing different information items andservices) to different users and to different types or groups of users,such that each user may have a customized experience and/or each type orgroup of user (e.g., all users, diabetic users, cardio users, healthcareprovider-user or payor-user, or the like) may have a different set ofinformation items or services available on the system. The system 1816may include or employ one or more suitable resource provisioning programor system for allocating appropriate resources to each user or type ofuser, based on a pre-defined authorization plan. Resource provisioningsystems are well known in connection with provisioning of electronicoffice resources (email, software programs under license, sensitivedata, etc.) in an office environment, for example, in a local areanetwork LAN for an office, company or firm. In one example embodiment,such resource provisioning systems is adapted to control access tomedical information and services on the DDMS 1816, based on the type ofuser and/or the identity of the user.

Upon entering successful verification of the user's identificationinformation and password, the user may be provided access to secure,personalized information stored on the DDMS 1816. For example, the usermay be provided access to a secure, personalized location in the DDMS1816 which has been assigned to the subject. This personalized locationmay be referred to as a personalized screen, a home screen, a home menu,a personalized page, etc. The personalized location may provide apersonalized home screen to the subject, including selectable icons ormenu items for selecting optional activities, including, for example, anoption to transfer device data from a subject's supported device 1812 tothe system 1816, manually enter additional data into the system 1816,modify the subject's custom settings, and/or view and print reports.Reports may include data specific to the subject's condition, includingbut not limited to, data obtained from the subject's subject supportdevice(s) 1812, data manually entered, data from medical libraries orother networked therapy management systems, data from the subjects orgroups of subjects, or the like. Where the reports includesubject-specific information and subject identification information, thereports may be generated from some or all subject data stored in asecure storage area (e.g., storage devices 1829) employed by thedatabase layer 1828.

The user may select an option to transfer (send) device data to themedical data management system 1816. If the system 1816 receives auser's request to transfer device data to the system, the system 1816may provide the user with step-by-step instructions on how to transferdata from the subject's supported device(s) 1812. For example, the DDMS1816 may have a plurality of different stored instruction sets forinstructing users how to download data from different types of subjectsupport devices, where each instruction set relates to a particular typeof subject supported device (e.g., pump, sensor, meter, or the like), aparticular manufacturer's version of a type of subject support device,or the like. Registration information received from the user duringregistration may include information regarding the type of subjectsupport device(s) 1812 used by the subject. The system 1816 employs thatinformation to select the stored instruction set(s) associated with theparticular subject's support device(s) 1812 for display to the user.

Other activities or resources available to the user on the system 1816may include an option for manually entering information to the DDMS/MDMS1816. For example, from the user's personalized menu or location, theuser may select an option to manually enter additional information intothe system 1816.

Further optional activities or resources may be available to the user onthe DDMS 1816. For example, from the user's personalized menu, the usermay select an option to receive data, software, software updates,treatment recommendations or other information from the system 1816 onthe subject's support device(s) 1812. If the system 1816 receives arequest from a user to receive data, software, software updates,treatment recommendations or other information, the system 1816 mayprovide the user with a list or other arrangement of multiple selectableicons or other indicia representing available data, software, softwareupdates or other information available to the user.

Yet further optional activities or resources may be available to theuser on the medical data management system 1816 including, for example,an option for the user to customize or otherwise further personalize theuser's personalized location or menu. In particular, from the user'spersonalized location, the user may select an option to customizeparameters for the user. In addition, the user may create profiles ofcustomizable parameters. When the system 1816 receives such a requestfrom a user, the system 1816 may provide the user with a list or otherarrangement of multiple selectable icons or other indicia representingparameters that may be modified to accommodate the user's preferences.When a user selects one or more of the icons or other indicia, thesystem 1816 may receive the user's request and makes the requestedmodification.

In one or more exemplary embodiments, for an individual patient in theDDMS, the computing device 1800 of the DDMS is configured to analyzethat patient's historical measurement data, historical delivery data,historical event log data, and any other historical or contextual dataassociated with the patient maintained in the database layer 1828 tosupport one or more of the processes of FIGS. 9-13. In this regard,machine learning, artificial intelligence, or similar mathematicalmodeling of the patient's physiological behavior or response may beperformed at the computing device 1800 to facilitate patient-specificcorrelations or predictions. Current measurement data, delivery data,and event log data associated with the patient along with currentcontextual data may be analyzed using the resultant models, either atthe computing device 1800 of the DDMS or another device 1812 todetermine probable events, behaviors, or responses by the patient inreal-time and perform corresponding delivery adjustments in a mannerthat is influenced by a correlative subset of the patient's historicaldata. As a result, patient outcomes may be improved while reducing theburden on the patient to make such patient-specific adjustments.

For the sake of brevity, conventional techniques related to glucosesensing and/or monitoring, sensor calibration and/or compensation,bolusing, machine learning and/or artificial intelligence, pharmodynamicmodeling, and other functional aspects of the subject matter may not bedescribed in detail herein. In addition, certain terminology may also beused in the herein for the purpose of reference only, and thus is notintended to be limiting. For example, terms such as “first,” “second,”and other such numerical terms referring to structures do not imply asequence or order unless clearly indicated by the context. The foregoingdescription may also refer to elements or nodes or features being“connected” or “coupled” together. As used herein, unless expresslystated otherwise, “coupled” means that one element/node/feature isdirectly or indirectly joined to (or directly or indirectly communicateswith) another element/node/feature, and not necessarily mechanically.

While at least one exemplary embodiment has been presented in theforegoing detailed description, it should be appreciated that a vastnumber of variations exist. It should also be appreciated that theexemplary embodiment or embodiments described herein are not intended tolimit the scope, applicability, or configuration of the claimed subjectmatter in any way. For example, the subject matter described herein isnot limited to the infusion devices and related systems describedherein. Moreover, the foregoing detailed description will provide thoseskilled in the art with a convenient road map for implementing thedescribed embodiment or embodiments. It should be understood thatvarious changes can be made in the function and arrangement of elementswithout departing from the scope defined by the claims, which includesknown equivalents and foreseeable equivalents at the time of filing thispatent application. Accordingly, details of the exemplary embodiments orother limitations described above should not be read into the claimsabsent a clear intention to the contrary.

What is claimed is:
 1. A method of operating an infusion device capableof delivering fluid to a patient, the fluid influencing a physiologicalcondition of the patient, the method comprising: obtaining, by a controlsystem associated with the infusion device, user input indicating anactivity by the patient; obtaining, by the control system, historicaldata for the patient corresponding to the activity; determining, by thecontrol system, a probable patient response corresponding to theactivity based at least in part on the historical data for the patient;determining, by the control system, an adjustment for delivering thefluid by the infusion device based at least in part on the probablepatient response; and operating, by the control system, the infusiondevice to deliver the fluid to the patient in accordance with theadjustment.
 2. The method of claim 1, further comprising identifying acurrent operational context for the infusion device, wherein determiningthe probable patient response comprises determining the probable patientresponse based at least in part on a correlation between the historicaldata corresponding to the activity and the current operational context.3. The method of claim 2, the historical data including historicalmeasurement data indicative of the physiological condition of thepatient and historical operational context data associated with thehistorical measurement data, the method further comprising determining amodel characterizing the probable patient response as a function of anoperational context for the infusion device based on a correlationbetween the historical measurement data and the historical operationalcontext data associated therewith, wherein determining the probablepatient response comprises applying the model to the current operationalcontext.
 4. The method of claim 1, further comprising: obtaining, by thecontrol system, a second user input indicating a characteristic of ameal; determining, by the control system, a meal bolus dosage based atleast in part on the characteristic, wherein determining the adjustmentcomprises adjusting the meal bolus dosage based at least in part on theprobable patient response.
 5. The method of claim 1, wherein:determining the adjustment comprises adjusting a target value for thephysiological condition based on a relationship between the probablepatient response and the target value, resulting in an adjusted targetvalue; and operating the infusion device to deliver the fluid to thepatient in accordance with the adjustment comprises: determining adosage command based at least in part on a difference between a currentmeasurement value for the physiological condition and the adjustedtarget value; and operating an actuation arrangement of the infusiondevice to deliver an amount of the fluid corresponding to the dosagecommand.
 6. The method of claim 1, wherein: obtaining the historicaldata comprises: identifying previous events corresponding to theactivity using event log data associated with the patient; and obtaininghistorical context data associated with the previous events andhistorical measurement data corresponding to the previous events,wherein the historical measurement data is indicative of thephysiological condition contemporaneous to the respective previousevents; and determining the probable patient response comprises:determining a model for a physiological response of the patient based ona correlation between the historical measurement data and historicalcontext data associated with the previous events; and applying the modelto a current operational context to obtain the probable patientresponse.
 7. The method of claim 6, wherein the historical context datacomprises timestamps associated with the previous events and the currentoperational context comprises a current time of day.
 8. The method ofclaim 1, the activity comprising exercise, wherein: obtaining thehistorical data comprises: identifying previous exercise events usingevent log data associated with the patient; and obtaining historicalcontext data associated with the previous exercise events and historicalmeasurement data corresponding to the previous exercise events, whereinthe historical measurement data is indicative of the physiologicalcondition contemporaneous to the respective previous exercise events;and determining the probable patient response comprises: determining amodel for a physiological response of the patient based on a correlationbetween the historical measurement data and historical context dataassociated with the previous exercise events; and applying the model toa current operational context to obtain the probable patient response.9. The method of claim 1, the activity comprising sleep, wherein:obtaining the historical data comprises obtaining historical measurementdata corresponding to a plurality of preceding overnight periods,wherein the historical measurement data is indicative of thephysiological condition during respective ones of the plurality ofpreceding overnight periods; and determining the probable patientresponse comprises determining the probable patient response based onthe historical measurement data corresponding to the plurality ofpreceding overnight periods.
 10. The method of claim 1, wherein: theuser input includes indication of an anticipated characteristic of theactivity; the historical data includes historical characteristicsassociated with previous instances of the activity and historicalmeasurement data corresponding to the previous instances of theactivity; and determining the probable patient response comprises:determining a model for a physiological response of the patient based ona correlation between the historical measurement data and the historicalcharacteristics associated with the previous instances of the activity;and applying the model to the anticipated characteristic of the activityto obtain the probable patient response.
 11. The method of claim 10,wherein the anticipated characteristic comprises an expected duration oran expected intensity of the activity.
 12. A computer-readable mediumhaving instructions stored thereon that are executable by the controlsystem associated with the infusion device to perform the method ofclaim
 1. 13. A method of operating an infusion device capable ofdelivering insulin to a patient, the method comprising: obtaining, by acontrol system associated with the infusion device, input indicative ofan activity by the patient in the future; obtaining, by the controlsystem, historical glucose measurement data for the patientcorresponding to previous instances of the activity; determining, by thecontrol system, a probable glycemic response to the activity for thepatient based at least in part on the historical glucose measurementdata for the previous instances of the activity; adjusting, by thecontrol system, a closed-loop control parameter based at least in parton the probable glycemic response; and after adjusting the closed-loopcontrol parameter: obtaining a sensor glucose measurement value for thepatient; and operating an actuation arrangement of the infusion deviceto deliver insulin to the patient based at least in part on the sensorglucose measurement value and the closed-loop control parameter.
 14. Themethod of claim 13, wherein: adjusting the closed-loop control parametercomprises adjusting a target glucose value for the patient to obtain anadjusted target glucose value; and operating the actuation arrangementcomprises determining a command for the actuation arrangement based atleast in part on a difference between the sensor glucose measurementvalue and the adjusted target glucose value.
 15. The method of claim 13,wherein determining the probable glycemic response to the activitycomprises: determining a predicted characteristic associated with theactivity based on an operational context and a correlation betweenhistorical characteristics and historical operational contextsassociated with the previous instances of the activity; and determiningthe probable glycemic response based on the predicted characteristic anda correlation between the historical glucose measurement data and thehistorical characteristics associated with the previous instances of theactivity.
 16. The method of claim 15, wherein: the operational contextcomprising a current time of day or a future time of day associated withthe activity; determining the predicted characteristic comprisesdetermining an expected duration of the activity or an expectedintensity of the activity based on the current time of day and acorrelation between historical times of day associated with the previousinstances of the activity and the respective duration or intensity ofthe respective previous instances of the activity; and determining theprobable glycemic response comprises: determining a model of aphysiological response by the patient to the activity as a function of aduration or an intensity of the activity and a time of day based oncorrelations between historical glucose measurement data associated withrespective ones of the previous instances of the activity, a respectiveduration or a respective intensity associated with the respective onesof the previous instances of the activity, and a respective time of dayassociated with the respective ones of the previous instances of theactivity; and determining the probable glycemic response by applying themodel to the predicted characteristic and the current time of day or thefuture time of day.
 17. The method of claim 16, further comprisingdetermining the future time of day associated with the activity based atleast in part on the historical times of day associated with theprevious instances of the activity.
 18. The method of claim 13, furthercomprising determining a predicted characteristic associated with theactivity based on the previous instances of the activity, whereinadjusting the closed-loop control parameter comprises temporarilyadjusting the closed-loop control parameter for a duration of timeinfluenced by the predicted characteristic.
 19. An infusion systemcomprising: an actuation arrangement operable to deliver fluid to auser, the fluid influencing a physiological condition of the user; auser interface to receive input indicative of an activity by the user inthe future; a data storage element to maintain historical datacorresponding to previous instances of the activity by the user; and acontrol system coupled to the actuation arrangement, the data storageelement and the user interface to determine a probable physiologicalresponse by the user to the activity based at least in part on thehistorical data, determine a fluid delivery adjustment based on theprobable physiological response, and operate the actuation arrangementto deliver the fluid to the user in accordance with the fluid deliveryadjustment.
 20. The infusion system of claim 19, wherein: the activitycomprises exercise or sleep; the fluid comprises insulin; and the fluiddelivery adjustment comprises an adjusted meal bolus or an adjustedclosed-loop control parameter.